Boosted Tree
Associated Constructors
Boosted Tree
Syntax: Boosted Tree (Y( column ), X( columns ))
Description: Constructs a predictive model by building a large, additive decision tree that is a sequence of smaller decision trees. Each of the trees is fit on the residuals of the previous tree.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
Columns
By
Syntax: obj << By( column(s) )
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
By( _bycol ),
Go
);
Factor
Syntax: obj << Factor( column(s) )
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
Freq
Syntax: obj << Freq( column )
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << New Column( "_freqcol", Numeric, Continuous, Formula( Random Integer( 1, 5 ) ) );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Freq( _freqcol ),
Go
);
Response
Syntax: obj << Response( column(s) )
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
Validation
Syntax: obj << Validation( column )
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
Weight
Syntax: obj << Weight( column )
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << New Column( "_weightcol", Numeric, Continuous, Formula( Random Beta( 1, 1 ) ) );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Weight( _weightcol ),
Go
);
X
Syntax: obj << X( column(s) )
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
Y
Syntax: obj << Y( column(s) )
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
Item Messages
Column Contributions
Syntax: obj << Column Contributions( state=0|1 )
Description: Shows or hides a report with each input column and its corresponding contribution to the fit.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Column Contributions( 1 );
Column Sampling Rate
Syntax: obj << Column Sampling Rate( number )
Description: Specifies the proportion of predictor columns to sample for each tree layer.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation Portion( 0.2 ),
Column Sampling Rate( 0.95 ),
Go
);
Decision Threshold
Syntax: obj << Decision Threshold( state = 0|1, Set Probability Threshold( number ) )
Description: Shows or hides the distribution of fitted probabilities and actual versus predicted tables for each model. You can change the probability threshold to explore how different thresholds affect the classification results.
dt = Open( "$Sample_Data/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Splits per Tree( 4 ),
Number of Layers( 171 ),
Learning Rate( 0.08 ),
Go
);
obj << Decision Threshold( 1 );
Early Stopping
Syntax: Early Stopping( state=0|1 )
Description: Stops iterating early when additional layers do not improve the validation statistic. On by default.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << New Column( "Holdback1", formula( Random Integer( 1, 3 ) ) );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
validation( :Holdback1 ),
Early Stopping( 1 ),
Go
);
Get Average Absolute Error Test
Syntax: obj << Get Average Absolute Error Test
Description: Returns the Mean Abs Dev statistic for the test set. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation 2 ),
Go
);
aabs = obj << Get Average Absolute Error Test;
Show( aabs );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation 2 ),
Go
);
aabs = obj << Get Average Absolute Error Test;
Show( aabs );
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation 2 ),
Split Best( 2 )
);
aabs = obj << Get Average Absolute Error Test;
Show( aabs );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
dt << Make Validation Column(
Training Set( .6 ),
Validation Set( .2 ),
Test Set( .2 ),
New Column Name( "Valid1" ),
Go
);
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Validation( :Valid1 ),
Split Best( 2 )
);
aabs = obj << Get Average Absolute Error Test;
Show( aabs );
Get Average Absolute Error Training
Syntax: obj << Get Average Absolute Error Training
Description: Returns the Mean Abs Dev statistic for the training set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Go
);
aabs = obj << Get Average Absolute Error Training;
Show( aabs );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Go
);
aabs = obj << Get Average Absolute Error Training;
Show( aabs );
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Split Best( 2 )
);
aabs = obj << Get Average Absolute Error Training;
Show( aabs );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 2 )
);
aabs = obj << Get Average Absolute Error Training;
Show( aabs );
Get Average Absolute Error Validation
Syntax: obj << Get Average Absolute Error Validation
Description: Returns the Mean Abs Dev statistic for the validation set. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation Portion( 0.2 ),
Go
);
aabs = obj << Get Average Absolute Error Validation;
Show( aabs );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation Portion( 0.2 ),
Go
);
aabs = obj << Get Average Absolute Error Validation;
Show( aabs );
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation Portion( 0.2 ),
Split Best( 2 )
);
aabs = obj << Get Average Absolute Error Validation;
Show( aabs );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Validation( :Validation ),
Split Best( 2 )
);
aabs = obj << Get Average Absolute Error Validation;
Show( aabs );
Get Average Log Error Test
Syntax: obj << Get Average Log Error Test
Description: Returns the average of -log(p), where p equals the probability of response attributed by the model that the response actually occurred, for the test set. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation 2 ),
Go
);
avg = obj << Get Average Log Error Test;
Show( avg );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation 2 ),
Go
);
avg = obj << Get Average Log Error Test;
Show( avg );
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation 2 ),
Method( "Decision Tree" ),
Go
);
avg = obj << Get Average Log Error Test;
Show( avg );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
dt << Make Validation Column(
Training Set( .6 ),
Validation Set( .2 ),
Test Set( .2 ),
New Column Name( "Valid1" ),
Go
);
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Validation( :Valid1 ),
Split Best( 2 )
);
avg = obj << Get Average Log Error Test;
Show( avg );
Get Average Log Error Training
Syntax: obj << Get Average Log Error Training
Description: Returns the average of -log(p), where p equals the probability of response attributed by the model that the response actually occurred, for the training set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Go
);
avg = obj << Get Average Log Error Training;
Show( avg );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Go
);
avg = obj << Get Average Log Error Training;
Show( avg );
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Split Best( 3 )
);
avg = obj << Get Average Log Error Training;
Show( avg );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 2 )
);
avg = obj << Get Average Log Error Training;
Show( avg );
Get Average Log Error Validation
Syntax: obj << Get Average Log Error Validation
Description: Returns the average of -log(p), where p equals the probability of response attributed by the model that the response actually occurred, for the validation set. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation Portion( 0.2 ),
Go
);
avg = obj << Get Average Log Error Validation;
Show( avg );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation Portion( 0.2 ),
Go
);
avg = obj << Get Average Log Error Validation;
Show( avg );
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation Portion( 0.2 ),
Method( "Decision Tree" ),
Go
);
avg = obj << Get Average Log Error Validation;
Show( avg );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Validation( :Validation ),
Split Best( 2 )
);
avg = obj << Get Average Log Error Validation;
Show( avg );
Get Confusion Matrix Test
Syntax: obj << Get Confusion Matrix Test
Description: Returns the confusion matrix for the test set. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
Y( :marital status ),
X( :sex, :age, :country, :type, :size ),
Validation( :Validation ),
Go
);
cm = obj << Get Confusion Matrix Test;
Show( cm );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Go
);
cm = obj << Get Confusion Matrix Test;
Show( cm );
Partition Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Split Best( 2 )
);
cm = obj << Get Confusion Matrix Test;
Show( cm );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
dt << Make Validation Column(
Training Set( .6 ),
Validation Set( .2 ),
Test Set( .2 ),
New Column Name( "Valid1" ),
Go
);
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Validation( :Valid1 ),
Split Best( 2 )
);
cm = obj << Get Confusion Matrix Test;
Show( cm );
Get Confusion Matrix Training
Syntax: obj << Get Confusion Matrix Training
Description: Returns the confusion matrix for the training set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
Y( :marital status ),
X( :sex, :age, :country, :type, :size ),
Validation( :Validation ),
Go
);
cm = obj << Get Confusion Matrix Training;
Show( cm );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Go
);
cm = obj << Get Confusion Matrix Training;
Show( cm );
Partition Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Split Best( 2 )
);
cm = obj << Get Confusion Matrix Training;
Show( cm );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 2 )
);
cm = obj << Get Confusion Matrix Training;
Show( cm );
Get Confusion Matrix Validation
Syntax: obj << Get Confusion Matrix Validation
Description: Returns the confusion matrix for the validation set. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
Y( :marital status ),
X( :sex, :age, :country, :type, :size ),
Validation( :Validation ),
Go
);
cm = obj << Get Confusion Matrix Validation;
Show( cm );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Go
);
cm = obj << Get Confusion Matrix Validation;
Show( cm );
Partition Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Split Best( 2 )
);
cm = obj << Get Confusion Matrix Validation;
Show( cm );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Validation( :Validation ),
Split Best( 2 )
);
cm = obj << Get Confusion Matrix Validation;
Show( cm );
Get Confusion Rates Test
Syntax: obj << Get Confusion Rates Test
Description: Returns the confusion rates for the test set. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
Y( :marital status ),
X( :sex, :age, :country, :type, :size ),
Validation( :Validation ),
Go
);
cr = obj << Get Confusion Rates Test;
Show( cr );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Go
);
cr = obj << Get Confusion Rates Test;
Show( cr );
Partition Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Split Best( 2 )
);
cr = obj << Get Confusion Rates Test;
Show( cr );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
dt << Make Validation Column(
Training Set( .6 ),
Validation Set( .2 ),
Test Set( .2 ),
New Column Name( "Valid1" ),
Go
);
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Validation( :Valid1 ),
Split Best( 2 )
);
cr = obj << Get Confusion Rates Test;
Show( cr );
Get Confusion Rates Training
Syntax: obj << Get Confusion Rates Training
Description: Returns the confusion rates for the training set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
Y( :marital status ),
X( :sex, :age, :country, :type, :size ),
Validation( :Validation ),
Go
);
cr = obj << Get Confusion Rates Training;
Show( cr );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Go
);
cr = obj << Get Confusion Rates Training;
Show( cr );
Partition Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Split Best( 2 )
);
cr = obj << Get Confusion Rates Training;
Show( cr );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 2 )
);
cr = obj << Get Confusion Rates Training;
Show( cr );
Get Confusion Rates Validation
Syntax: obj << Get Confusion Rates Validation
Description: Returns the confusion rates for the validation set. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
Y( :marital status ),
X( :sex, :age, :country, :type, :size ),
Validation( :Validation ),
Go
);
cr = obj << Get Confusion Rates Validation;
Show( cr );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Go
);
cr = obj << Get Confusion Rates Validation;
Show( cr );
Partition Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Split Best( 2 )
);
cr = obj << Get Confusion Rates Validation;
Show( cr );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Validation( :Validation ),
Split Best( 2 )
);
cr = obj << Get Confusion Rates Validation;
Show( cr );
Get Gen RSquare Test
Syntax: obj << Get Gen RSquare Test
Description: Returns the generalized RSquare for the test set. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
Y( :sex ),
X( :marital status, :age, :country, :type, :size ),
Validation( :Validation ),
Go
);
r = obj << Get Gen RSquare Test;
Show( r );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Go
);
r = obj << Get Gen RSquare Test;
Show( r );
Partition Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Split Best( 2 )
);
r = obj << Get Gen RSquare Test;
Show( r );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
dt << Make Validation Column(
Training Set( .6 ),
Validation Set( .2 ),
Test Set( .2 ),
New Column Name( "Valid1" ),
Go
);
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Validation( :Valid1 ),
Split Best( 2 )
);
r = obj << Get Gen RSquare Test;
Show( r );
Get Gen RSquare Training
Syntax: obj << Get Gen RSquare Training
Description: Returns the generalized RSquare for the training set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
Y( :sex ),
X( :marital status, :age, :country, :type, :size ),
Validation( :Validation ),
Go
);
r = obj << Get Gen RSquare Training;
Show( r );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Go
);
r = obj << Get Gen RSquare Training;
Show( r );
Partition Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Split Best( 2 )
);
r = obj << Get Gen RSquare Training;
Show( r );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 2 )
);
r = obj << Get Gen RSquare Training;
Show( r );
Get Gen RSquare Validation
Syntax: obj << Get Gen RSquare Validation
Description: Returns the generalized RSquare for the validation set. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
Y( :sex ),
X( :marital status, :age, :country, :type, :size ),
Validation( :Validation ),
Go
);
r = obj << Get Gen RSquare Validation;
Show( r );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Go
);
r = obj << Get Gen RSquare Validation;
Show( r );
Partition Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation( :Validation ),
Split Best( 2 )
);
r = obj << Get Gen RSquare Validation;
Show( r );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Validation( :Validation ),
Split Best( 2 )
);
r = obj << Get Gen RSquare Validation;
Show( r );
Get MM SAS DATA Step
Syntax: obj << Get MM SAS DATA Step
Description: Creates SAS code that you can register in the SAS Model Manager and returns it to the Log window.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
code = obj << Get MM SAS Data Step;
Get MM Tolerant SAS DATA Step
Syntax: obj << Get MM Tolerant SAS DATA Step
Description: Creates SAS code for data that includes missing values that you can register in the SAS Model Manager and returns it to the Log window.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
code = obj << Get MM Tolerant SAS Data Step;
Get Measures
Syntax: obj << Get Measures
Description: Returns summary measures of fit from the model.
JMP Version Added: 16
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Get Measures;
Get Microseconds
Syntax: obj << Get Microseconds
Description: Returns the number of microseconds used to complete the analysis.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
time = obj << Get Microseconds;
Show( time );
Get Misclassification Rate Test
Syntax: obj << Get Misclassification Rate Test
Description: Returns the misclassification rate for the test set. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation 2 ),
Go
);
rate = obj << Get Misclassification Rate Test;
Show( rate );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation 2 ),
Go
);
rate = obj << Get Misclassification Rate Test;
Show( rate );
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation 2 ),
Split Best( 2 )
);
rate = obj << Get Misclassification Rate Test;
Show( rate );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
dt << Make Validation Column(
Training Set( .6 ),
Validation Set( .2 ),
Test Set( .2 ),
New Column Name( "Valid1" ),
Go
);
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Validation( :Valid1 ),
Split Best( 2 )
);
rate = obj << Get Misclassification Rate Test;
Show( rate );
Get Misclassification Rate Training
Syntax: obj << Get Misclassification Rate Training
Description: Returns the misclassification rate for the training set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Go
);
rate = obj << Get Misclassification Rate Training;
Show( rate );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Go
);
rate = obj << Get Misclassification Rate Training;
Show( rate );
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Method( "Decision Tree" )
);
obj << Split Best( 2 );
rate = obj << Get Misclassification Rate Training;
Show( rate );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Split Best( 2 )
);
rate = obj << Get Misclassification Rate Training;
Show( rate );
Get Misclassification Rate Validation
Syntax: obj << Get Misclassification Rate Validation
Description: Returns the misclassification rate for the validation set. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << New Column( "Holdback1", formula( Random Integer( 1, 3 ) ) );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
validation( :Holdback1 ),
Go
);
rate = obj << Get Misclassification Rate Validation;
Show( rate );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << New Column( "Holdback1", formula( Random Integer( 1, 3 ) ) );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
validation( :Holdback1 ),
Go
);
rate = obj << Get Misclassification Rate Validation;
Show( rate );
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << New Column( "Holdback1", formula( Random Integer( 1, 3 ) ) );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
validation( :Holdback1 ),
Method( "Decision Tree" ),
Go
);
rate = obj << Get Misclassification Rate Validation;
Show( rate );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Validation( :Validation ),
Split Best( 2 )
);
rate = obj << Get Misclassification Rate Validation;
Show( rate );
Get Precision Recall Area Test
Syntax: obj << Get Precision Recall Area Test
Description: Returns the area under the precision-recall curve for the test set. The precision-recall curve must be displayed before the area is computed. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation 2 ),
Go
);
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Test;
Show( area );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation 2 ),
Go
);
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Test;
Show( area );
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation 2 ),
Method( "Decision Tree" ),
Go
);
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Test;
Show( area );
Get Precision Recall Area Training
Syntax: obj << Get Precision Recall Area Training
Description: Returns the area under the precision-recall curve for the training set. The precision-recall curve must be displayed before the area is computed.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Go
);
obj << Show Tree( 0 );
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Training;
Show( area );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Go
);
obj << Show Tree( 0 );
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Training;
Show( area );
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Split Best( 2 )
);
obj << Show Tree( 0 );
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Training;
Show( area );
Get Precision Recall Area Validation
Syntax: obj << Get Precision Recall Area Validation
Description: Returns the area under the precision-recall curve for the validation set. The precision-recall curve must be displayed before the area is computed. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation Portion( 0.2 ),
Go
);
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Validation;
Show( area );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation Portion( 0.2 ),
Go
);
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Validation;
Show( area );
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation Portion( 0.2 ),
Method( "Decision Tree" ),
Go
);
obj << Precision Recall Curve;
area = obj << Get Precision Recall Area Validation;
Show( area );
Get Prediction Formula
Syntax: obj << Get Prediction Formula
Description: Constructs a script to create a prediction formula column and returns it.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Get Prediction Formula;
Get RMS Error Test
Syntax: obj << Get RMS Error Test
Description: Returns the square root of the mean square of the test errors. Available only when using a validation set.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
rms = obj << Get RMS Error Test;
Show( rms );
Get RMS Error Training
Syntax: obj << Get RMS Error Training
Description: Returns the square root of the mean square of the training errors.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
rms = obj << Get RMS Error Training;
Show( rms );
Get RMS Error Validation
Syntax: obj << Get RMS Error Validation
Description: Returns the square root of the mean square of the validation errors. Available only when using a validation set.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
rms = obj << Get RMS Error Validation;
Show( rms );
Get ROC Area Test
Syntax: obj << Get ROC Area Test
Description: Returns the area under the Receiver Operator Characteristic (ROC) curve for the test data. The ROC curve needs to be displayed before the area is computed. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation 2 ),
Go
);
obj << ROC Curve;
area = obj << Get ROC Area Test;
Show( area );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation 2 ),
Go
);
obj << ROC Curve;
area = obj << Get ROC Area Test;
Show( area );
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << Make Validation Column( Training Set( .6 ), Validation Set( .2 ), Test Set( .2 ), Go );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation 2 ),
Method( "Decision Tree" ),
Go
);
obj << ROC Curve;
area = obj << Get ROC Area Test;
Show( area );
Get ROC Area Training
Syntax: obj << Get ROC Area Training
Description: Returns the area under the Receiver Operator Characteristic (ROC) curve for the training data set. The ROC curve needs to be displayed before the area is computed.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Go
);
obj << Show Tree( 0 );
obj << ROC Curve;
area = obj << Get ROC Area Training;
Show( area );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Go
);
obj << Show Tree( 0 );
obj << ROC Curve;
area = obj << Get ROC Area Training;
Show( area );
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Split Best( 2 )
);
obj << Show Tree( 0 );
obj << ROC Curve;
area = obj << Get ROC Area Training;
Show( area );
Get ROC Area Validation
Syntax: obj << Get ROC Area Validation
Description: Returns the area under the Receiver Operator Characteristic (ROC) curve for the validation data set. The ROC curve needs to be displayed before the area is computed. Available only when using a validation set.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation Portion( 0.2 ),
Go
);
obj << ROC Curve;
area = obj << Get ROC Area Validation;
Show( area );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation Portion( 0.2 ),
Go
);
obj << ROC Curve;
area = obj << Get ROC Area Validation;
Show( area );
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation Portion( 0.2 ),
Method( "Decision Tree" ),
Go
);
obj << ROC Curve;
area = obj << Get ROC Area Validation;
Show( area );
Get RSquare Test
Syntax: obj << Get RSquare Test
Description: Returns the RSquare for the test set. Available only when using a validation set.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
r = obj << Get RSquare Test;
Show( r );
Get RSquare Training
Syntax: obj << Get RSquare Training
Description: Returns the RSquare for the training set.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
r = obj << Get RSquare Training;
Show( r );
Get RSquare Validation
Syntax: obj << Get RSquare Validation
Description: Returns the RSquare for the validation set. Available only when using a validation set.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
r = obj << Get RSquare Validation;
Show( r );
Get SAS DATA Step
Syntax: obj << Get SAS DATA Step
Description: Creates a SAS DATA step to score the data and returns it to the Log window.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
code = obj << Get SAS Data Step;
Get Seconds
Syntax: obj << Get Seconds
Description: Returns the number of seconds used to complete the analysis.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
time = obj << Get Seconds;
Show( time );
Get Tolerant Prediction Formula
Syntax: obj << Get Tolerant Prediction Formula
Description: Constructs a script to create a tolerant prediction formula column and returns it.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Get Tolerant Prediction Formula;
Get Tolerant SAS DATA Step
Syntax: obj << Get Tolerant SAS DATA Step
Description: Creates a SAS DATA step to score data that includes missing values and returns it to the Log window. Missing values are randomly assigned to a tree branch.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
code = obj << Get Tolerant SAS Data Step;
Go
Syntax: obj << Go
Description: Begins iterating after all parameters have been set.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
Informative Missing
Syntax: obj = Boosted Tree(...Informative Missing( state=0|1 )...)
Description: For categorical variables, treats missing as a category. For continuous variables, treats missing as either low or high, whichever fits better. On by default.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt:age[3] = .;
obj = dt << Boosted Tree( Y( :height ), X( :age ), Informative Missing( 0 ), Go );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt:age[3] = .;
obj = dt << Bootstrap Forest( Y( :height ), X( :age ), Informative Missing( 0 ), Go );
Partition Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt:age[3] = .;
obj = dt << Partition( Y( :height ), X( :age ), Informative Missing( 0 ) );
obj << Split Best( 1 );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
dt:Age[3] = .;
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Informative Missing( 0 ),
Split Best( 3 )
);
Learning Rate
Syntax: Learning Rate( fraction )
Description: Sets the learning rate used in the estimate. Default is 0.1. ".1" by default.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Learning Rate( 0.2 ),
Go
);
Lift Curve
Syntax: obj << Lift Curve( state=0|1 )
Description: Shows or hides the Lift Curve plot. A lift curve plots the lift versus the portion of the observations and provides another view of the predictive ability of a model. If you used validation, a plot is shown for each of the training, validation, and test sets.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Lift Curve( 1 );
Make SAS DATA Step
Syntax: obj << Make SAS DATA Step
Description: Creates a SAS DATA step to score the data and returns it to a script window.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Make SAS Data Step;
Make Tolerant SAS DATA Step
Syntax: obj << Make Tolerant SAS DATA Step
Description: Creates a SAS DATA step to score data that includes missing values and returns it to a script window. Missing values are randomly assigned to a tree branch.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Make Tolerant SAS Data Step;
Maximum Depth
Syntax: obj << Maximum Depth( number )
Description: Restricts tree size by depth instead of number of nodes.
Method
Syntax: Method( "Boosted Tree" )
Description: Determines the method used for partitioning the data. Decision Tree is the default.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
Minimum Size Split
Syntax: Minimum Size Split( number )
Description: Sets the minimum number of observations for considering splits used in the estimate. Default is 5.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Minimum Size Split( 10 ),
Go
);
Multithreading
Syntax: Multithreading( state=0|1 )
Description: Divides up the calculations among the available threads on the machine. On by default.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Multithreading( 1 ),
Go
);
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Multithreading( 1 ),
Go
);
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Multithreading( 1 ),
Split Best( 2 )
);
Number of Layers
Syntax: Number of Layers( number )
Description: Sets the number of layers used in the estimate. Default is 50. "100" by default.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Number of Layers( 20 ),
Go
);
Ordinal Restricts Order
Syntax: obj = Boosted Tree(...Ordinal Restricts Order( state=0|1 )...)
Description: For ordinal columns, considers only splits that preserve order. On by default.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Boosted Tree( Y( :height ), X( :age ), Ordinal Restricts Order( 1 ), Go );
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bootstrap Forest( Y( :height ), X( :age ), Ordinal Restricts Order( 1 ), Go );
Partition Example
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Partition( Y( :height ), X( :age ), Ordinal Restricts Order( 1 ) );
obj << Split Best( 3 );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Ordinal Restricts Order( 1 ),
Split Best( 2 )
);
Overfit Penalty
Syntax: Overfit Penalty( fraction )
Description: Sets the overfit penalty, which introduces bias to move the probabilities away from zero for models with a categorical response. Default is 0.0001.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Overfit Penalty( 0.0005 ),
Go
);
Plot Actual by Predicted
Syntax: obj << Plot Actual by Predicted( state=0|1 )
Description: Shows or hides a plot using the training data with the predicted values on the X axis and actual values on the Y axis.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Plot Actual by Predicted( 1 );
Precision Recall Curve
Syntax: obj << Precision Recall Curve( state=0|1 )
Description: Shows or hides the Precision-Recall Curve plot that contains a curve for each level of the response variable. A precision-recall curve plots the precision values against the recall values at a variety of thresholds. If you used validation, a plot is shown for each of the training, validation, and test sets.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Go
);
obj << Precision Recall Curve( 1 );
Profiler
Syntax: obj << Profiler( state=0|1 )
Description: Shows or hides the prediction profiler, which is used to graphically explore the prediction equation by slicing it one factor at a time. The prediction profiler contains features for optimization.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Go
);
obj << Profiler( 1 );
Publish Prediction Formula
Syntax: obj << Publish Prediction Formula
Description: Creates prediction formulas and saves them as formula column scripts in the Formula Depot platform.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Publish Prediction Formula;
Publish Tolerant Prediction Formula
Syntax: obj << Publish Tolerant Prediction Formula
Description: Builds a prediction formula that predicts even when there are missing values and publishes it as a formula column script in Formula Depot.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Publish Tolerant Prediction Formula;
ROC Curve
Syntax: obj << ROC Curve( state=0|1 )
Description: Shows or hides the Receiver Operating Characteristic (ROC) curve for each level of the response variable. The ROC curve is a plot of sensitivity versus (1 - specificity). If you used validation, a plot is shown for each of the training, validation, and test sets.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << ROC Curve( 1 );
Row Sampling Rate
Syntax: obj << Row Sampling Rate( number )
Description: Specifies the proportion of training rows to sample for each tree layer.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation Portion( 0.2 ),
Row Sampling Rate( 0.95 ),
Go
);
Save Cumulative Details
Syntax: obj << Save Cumulative Details
Description: Saves the validation RSquare along with the tree number in a new data table. Available only when using a validation set.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Save Cumulative Details;
Save Offset Estimates
Syntax: obj << Save Offset Estimates
Description: Saves the offset estimates to a new column in the data table. Available only for categorical responses.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Go
);
obj << Save Offset Estimates;
Save Predicteds
Syntax: obj << Save Predicteds
Description: Saves the predicted values in a new column in the data table.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Save Predicteds;
Save Prediction Formula
Syntax: obj << Save Prediction Formula
Description: Saves the prediction formula in a new column in the data table.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Save Prediction Formula;
Save Residuals
Syntax: obj << Save Residuals
Description: Saves the residuals in a new column in the data table.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Save Residuals;
Save Tolerant Prediction Formula
Syntax: obj << Save Tolerant Prediction Formula
Description: Save a formula that predicts even when there are missing values in a new column in the data table.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Save Tolerant Prediction Formula;
Save Tree Details
Syntax: obj << Save Tree Details
Description: Saves the layer, split, label, and estimate for each layer-split combination in a new data table.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Save Tree Details;
Set Random Seed
Syntax: obj << Set Random Seed( number )
Description: Specifies a random seed to reproduce the results for future launches of the platform.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Set Random Seed( 1234 ),
Go
);
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Bootstrap Forest(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Set Random Seed( 1234 ),
Go
);
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Partition(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Set Random Seed( 1234 ),
Split Best( 2 )
);
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Set Random Seed( 1234 ),
Split Best( 2 )
);
Show Trees
Syntax: obj << Show Trees( "None"|"Show names"|"Show names categories"|"Show names categories estimates" )
Description: Shows a list of trees at each layer, with names only, names and categories, or names, categories and estimates at each node.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Show Trees( Show names categories );
(obj << Report)["Tree Views"] << Close( 0 );
(obj << Report)["Layer4"] << Close( 0 );
Specify Profit Matrix
Syntax: obj << Specify Profit Matrix
Description: Enables you to specify profits or costs associated with correct or incorrect classification decisions.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y Binary ),
X( :Age, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Specify Profit Matrix( [1 -1, -1 1, . .], "0", "1", "Undecided" ),
Go
);
Splits per Tree
Syntax: Splits Per Tree( number )
Description: Sets the number of splits per tree used in the estimate. Default is 3. "3" by default.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Splits Per Tree( 2 ),
Go
);
Tuning Design Table
Syntax: Tuning Design Table( "table name" )
Description: A table of tuning parameters to run with, supporting: Splits Per Tree, Learning Rate, Row Sampling Rate, Column Sampling Rate, Number of Layers, Minimum Size Split
Use Excluded Rows for Validation
Syntax: obj = Boosted Tree(...Use Excluded Rows for Validation( state=0|1 )...)
Description: Uses the excluded rows in the data table to create a validation set. This option appears in the launch window only if you are using standard JMP and there are excluded rows.
JMP Version Added: 15
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
For Each( {i}, 10 :: 200 :: 10, Row State( i ) = Excluded State( 1 ) );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Use Excluded Rows for Validation( 1 ),
Go
);
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
For Each( {i}, 10 :: 200 :: 10, Row State( i ) = Excluded State( 1 ) );
obj = dt << Bootstrap Forest(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Use Excluded Rows for Validation( 1 ),
Go
);
Partition Example
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
For Each( {i}, 10 :: 200 :: 10, Row State( i ) = Excluded State( 1 ) );
obj = dt << Partition(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Use Excluded Rows for Validation( 1 )
);
obj << Split Best( 5 );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
For Each( {i}, 10 :: 200 :: 10, Row State( i ) = Excluded State( 1 ) );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Use Excluded Rows for Validation( 1 ),
Split Best( 2 )
);
Validation Portion
Syntax: obj = Boosted Tree(...Validation Portion( fraction=0 )...)
Description: Forms a validation set by randomly selecting rows with each row having probability p (fraction) of being selected. "0" by default.
Boosted Tree Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Boosted Tree(
Y( :marital status ),
X( :sex, :country, :age, :type, :size ),
Validation Portion( 0.2 ),
Go
);
Bootstrap Forest Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Bootstrap Forest(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation Portion( 0.2 ),
Go
);
Partition Example
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Partition(
Y( :country ),
X( :sex, :marital status, :age, :type, :size ),
Validation Portion( 0.2 )
);
obj << Split Best( 2 );
Uplift Example
dt = Open( "$SAMPLE_DATA/Hair Care Product.jmp" );
obj = dt << Uplift(
Y( :Purchase ),
X( :Gender, :Age, :Hair Color, :U.S. Region, :Residence ),
Treatment( :Promotion ),
Validation Portion( 0.2 ),
Go
);
Shared Item Messages
Action
Syntax: obj << Action
Description: All-purpose trapdoor within a platform to insert expressions to evaluate. Temporarily sets the DisplayBox and DataTable contexts to the Platform.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Bivariate(
Y( :height ),
X( :weight ),
Action( Distribution( Y( :height, :weight ), Histograms Only ) )
);
Apply Preset
Syntax: Apply Preset( preset ); Apply Preset( source, label, <Folder( folder {, folder2, ...} )> )
Description: Apply a previously created preset to the object, updating the options and customizations to match the saved settings.
JMP Version Added: 18
Anonymous preset
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ), t Test( 1 ) );
preset = obj << New Preset();
dt2 = Open( "$SAMPLE_DATA/Dogs.jmp" );
obj2 = dt2 << Oneway( Y( :LogHist0 ), X( :drug ) );
Wait( 1 );
obj2 << Apply Preset( preset );
Search by name
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ) );
Wait( 1 );
obj << Apply Preset( "Sample Presets", "Compare Distributions" );
Search within folder(s)
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ) );
Wait( 1 );
obj << Apply Preset( "Sample Presets", "t-Tests", Folder( "Compare Means" ) );
Automatic Recalc
Syntax: obj << Automatic Recalc( state=0|1 )
Description: Redoes the analysis automatically for exclude and data changes. If the Automatic Recalc option is turned on, you should consider using Wait(0) commands to ensure that the exclude and data changes take effect before the recalculation.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Automatic Recalc( 1 );
dt << Select Rows( 5 ) << Exclude( 1 );
Column Switcher
Syntax: obj << Column Switcher(column reference, {column reference, ...}, < Title(title) >, < Close Outline(0|1) >, < Retain Axis Settings(0|1) >, < Layout(0|1) >)
Description: Adds a control panel for changing the platform's variables
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Contingency( Y( :size ), X( :marital status ) );
ColumnSwitcherObject = obj << Column Switcher(
:marital status,
{:sex, :country, :marital status}
);
Copy Script
Syntax: obj << Copy Script
Description: Create a JSL script to produce this analysis, and put it on the clipboard.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Copy Script;
Data Table Window
Syntax: obj << Data Table Window
Description: Move the data table window for this analysis to the front.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Data Table Window;
Get By Levels
Syntax: obj << Get By Levels
Description: Returns an associative array mapping the by group columns to their values.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( X( :height ), Y( :weight ), By( :sex ) );
biv << Get By Levels;
Get Container
Syntax: obj << Get Container
Description: Returns a reference to the container box that holds the content for the object.
General
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
t = obj << Get Container;
Show( (t << XPath( "//OutlineBox" )) << Get Title );
Platform with Filter
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
gb = Graph Builder(
Show Control Panel( 0 ),
Variables( X( :height ), Y( :weight ) ),
Elements( Points( X, Y, Legend( 1 ) ), Smoother( X, Y, Legend( 2 ) ) ),
Local Data Filter(
Add Filter(
columns( :age, :sex, :height ),
Where( :age == {12, 13, 14} ),
Where( :sex == "F" ),
Where( :height >= 55 ),
Display( :age, N Items( 6 ) )
)
)
);
New Window( "platform boxes",
H List Box(
Outline Box( "Report(platform)", Report( gb ) << Get Picture ),
Outline Box( "platform << Get Container", (gb << Get Container) << Get Picture )
)
);
Get Data Table
Syntax: obj << Get Data Table
Description: Returns a reference to the data table.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
t = obj << Get Datatable;
Show( N Rows( t ) );
Get Script
Syntax: obj << Get Script
Description: Creates a script (JSL) to produce this analysis and returns it as an expression.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
t = obj << Get Script;
Show( t );
Get Script With Data Table
Syntax: obj << Get Script With Data Table
Description: Creates a script(JSL) to produce this analysis specifically referencing this data table and returns it as an expression.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
t = obj << Get Script With Data Table;
Show( t );
Get Timing
Syntax: obj << Get Timing
Description: Times the platform launch.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
t = obj << Get Timing;
Show( t );
Get Web Support
Syntax: obj << Get Web Support
Description: Return a number indicating the level of Interactive HTML support for the display object. 1 means some or all elements are supported. 0 means no support.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
s = obj << Get Web Support();
Show( s );
Get Where Expr
Syntax: obj << Get Where Expr
Description: Returns the Where expression for the data subset, if the platform was launched with By() or Where(). Otherwise, returns Empty()
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( X( :height ), Y( :weight ), By( :sex ) );
biv2 = dt << Bivariate( X( :height ), Y( :weight ), Where( :age < 14 & :height > 60 ) );
Show( biv[1] << Get Where Expr, biv2 << Get Where Expr );
Ignore Platform Preferences
Syntax: Ignore Platform Preferences( state=0|1 )
Description: Ignores the current settings of the platform's preferences. The message is ignored when sent to the platform after creation.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Bivariate(
Ignore Platform Preferences( 1 ),
Y( :height ),
X( :weight ),
Action( Distribution( Y( :height, :weight ), Histograms Only ) )
);
Local Data Filter
Syntax: obj << Local Data Filter
Description: To filter data to specific groups or ranges, but local to this platform
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dt << Distribution(
Nominal Distribution( Column( :country ) ),
Local Data Filter(
Add Filter( columns( :sex ), Where( :sex == "Female" ) ),
Mode( Show( 1 ), Include( 1 ) )
)
);
New JSL Preset
Syntax: New JSL Preset( preset )
Description: For testing purposes, create a preset directly from a JSL expression. Like <<New Preset, it will return a Platform Preset that can be applied using <<Apply Preset. But it allows you to specify the full JSL expression for the preset to test outside of normal operation. You will get an Assert on apply if the platform names do not match, but that is expected.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
preset = obj << New JSL Preset( Oneway( Y( :A ), X( :B ), Each Pair( 1 ) ) );
Wait( 1 );
obj << Apply Preset( preset );
New Preset
Syntax: obj = New Preset()
Description: Create an anonymous preset representing the options and customizations applied to the object. This object can be passed to Apply Preset to copy the settings to another object of the same type.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ), t Test( 1 ) );
preset = obj << New Preset();
Paste Local Data Filter
Syntax: obj << Paste Local Data Filter
Description: Apply the local data filter from the clipboard to the current report.
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
dist = Distribution( Continuous Distribution( Column( :POP ) ) );
filter = dist << Local Data Filter(
Add Filter( columns( :Region ), Where( :Region == "MW" ) )
);
filter << Copy Local Data Filter;
dist2 = Distribution( Continuous Distribution( Column( :Lead ) ) );
Wait( 1 );
dist2 << Paste Local Data Filter;
Redo Analysis
Syntax: obj << Redo Analysis
Description: Rerun this same analysis in a new window. The analysis will be different if the data has changed.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Redo Analysis;
Relaunch Analysis
Syntax: obj << Relaunch Analysis
Description: Opens the platform launch window and recalls the settings that were used to create the report.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Relaunch Analysis;
Remove Column Switcher
Syntax: obj << Remove Column Switcher
Description: Removes the most recent Column Switcher that has been added to the platform.
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
obj = dt << Contingency( Y( :size ), X( :marital status ) );
ColumnSwitcherObject = obj << Column Switcher(
:marital status,
{:sex, :country, :marital status}
);
Wait( 2 );
obj << Remove Column Switcher;
Remove Local Data Filter
Syntax: obj << Remove Local Data Filter
Description: If a local data filter has been created, this removes it and restores the platform to use all the data in the data table directly
dt = Open( "$SAMPLE_DATA/Car Poll.jmp" );
dist = dt << Distribution(
Nominal Distribution( Column( :country ) ),
Local Data Filter(
Add Filter( columns( :sex ), Where( :sex == "Female" ) ),
Mode( Show( 1 ), Include( 1 ) )
)
);
Wait( 2 );
dist << remove local data filter;
Render Preset
Syntax: Render Preset( preset )
Description: For testing purposes, show the platform rerun script that would be used when applying a platform preset to the platform in the log. No changes are made to the platform.
JMP Version Added: 18
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :Height ), X( :Age ) );
obj << Render Preset( Expr( Oneway( Y( :A ), X( :B ), Each Pair( 1 ) ) ) );
Report
Syntax: obj << Report;Report( obj )
Description: Returns a reference to the report object.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
r = obj << Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );
Report View
Syntax: obj << Report View( "Full"|"Summary" )
Description: The report view determines the level of detail visible in a platform report. Full shows all of the detail, while Summary shows only select content, dependent on the platform. For customized behavior, display boxes support a <<Set Summary Behavior message.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Report View( "Summary" );
Save Script for All Objects
Syntax: obj << Save Script for All Objects
Description: Creates a script for all report objects in the window and appends it to the current Script window. This option is useful when you have multiple reports in the window.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Save Script for All Objects;
Save Script for All Objects To Data Table
Syntax: obj << Save Script for All Objects To Data Table( <name> )
Description: Saves a script for all report objects to the current data table. This option is useful when you have multiple reports in the window. The script is named after the first platform unless you specify the script name in quotes.
Example 1
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
By( _bycol ),
Go
);
obj[1] << Save Script for All Objects To Data Table;
Example 2
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
dt << New Column( "_bycol",
Character,
Nominal,
set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
By( _bycol ),
Go
);
obj[1] << Save Script for All Objects To Data Table( "My Script" );
Save Script to Data Table
Syntax: Save Script to Data Table( <name>, < <<Prompt(0|1)>, < <<Replace(0|1)> );
Description: Create a JSL script to produce this analysis, and save it as a table property in the data table.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Save Script to Data Table( "My Analysis", <<Prompt( 0 ), <<Replace( 0 ) );
Save Script to Journal
Syntax: obj << Save Script to Journal
Description: Create a JSL script to produce this analysis, and add a Button to the journal containing this script.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Save Script to Journal;
Save Script to Report
Syntax: obj << Save Script to Report
Description: Create a JSL script to produce this analysis, and show it in the report itself. Useful to preserve a printed record of what was done.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Save Script to Report;
Save Script to Script Window
Syntax: obj << Save Script to Script Window
Description: Create a JSL script to produce this analysis, and append it to the current Script text window.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Save Script to Script Window;
SendToByGroup
Syntax: SendToByGroup( {":Column == level"}, command );
Description: Sends platform commands or display customization commands to each level of a by-group.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Distribution(
By( :Sex ),
SendToByGroup(
{:sex == "F"},
Continuous Distribution( Column( :weight ), Normal Quantile Plot( 1 ) )
),
SendToByGroup( {:sex == "M"}, Continuous Distribution( Column( :weight ) ) )
);
SendToEmbeddedScriptable
Syntax: SendToEmbeddedScriptable( Dispatch( "Outline name", "Element name", command );
Description: SendToEmbeddedScriptable restores settings of embedded scriptable objects.
dt = Open( "$SAMPLE_DATA/Reliability/Fan.jmp" );
dt << Life Distribution(
Y( :Time ),
Censor( :Censor ),
Censor Code( 1 ),
<<Fit Weibull,
SendToEmbeddedScriptable(
Dispatch(
{"Statistics", "Parametric Estimate - Weibull", "Profilers", "Density Profiler"},
{1, Confidence Intervals( 0 ), Term Value( Time( 6000, Lock( 0 ), Show( 1 ) ) )}
)
)
);
SendToReport
Syntax: SendToReport( Dispatch( "Outline name", "Element name", Element type, command );
Description: Send To Report is used in tandem with the Dispatch command to customize the appearance of a report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Distribution(
Nominal Distribution( Column( :age ) ),
Continuous Distribution( Column( :weight ) ),
SendToReport( Dispatch( "age", "Distrib Nom Hist", FrameBox, {Frame Size( 178, 318 )} ) )
);
Sync to Data Table Changes
Syntax: obj << Sync to Data Table Changes
Description: Sync with the exclude and data changes that have been made.
dt = Open( "$SAMPLE_DATA/Cities.jmp" );
dist = Distribution( Continuous Distribution( Column( :POP ) ) );
Wait( 1 );
dt << Delete Rows( dt << Get Rows Where( :Region == "W" ) );
dist << Sync To Data Table Changes;
Title
Syntax: obj << Title( "new title" )
Description: Sets the title of the platform.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
obj << Title( "My Platform" );
Top Report
Syntax: obj << Top Report
Description: Returns a reference to the root node in the report.
dt = Open( "$SAMPLE_DATA/Diabetes.jmp" );
obj = dt << Boosted Tree(
Y( :Y ),
X( :Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG, :Glucose ),
Validation( :Validation ),
Go
);
r = obj << Top Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );
View Web XML
Syntax: obj << View Web XML
Description: Returns the XML code that is used to create the interactive HTML report.
dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
xml = obj << View Web XML;