XGBoost

Associated Constructors

XGBoost

Syntax: XGBoost(Y( columns ), X( columns ))

Description: Predictive modeling interface to eXtreme Gradient Boosted trees.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );

Columns

Censor

Syntax: obj << Censor( column )

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );

Factor

Syntax: obj << Factor( column(s) )

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );

Freq

Syntax: obj << Freq( column )

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_freqcol", Numeric, Continuous, Formula( Random Integer( 1, 5 ) ) );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );

Response

Syntax: obj << Response( column(s) )

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );

Validation

Syntax: obj << Validation( column(s) )

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );

Weight

Syntax: obj << Weight( column )

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_weightcol", Numeric, Continuous, Formula( Random Beta( 1, 1 ) ) );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );

X

Syntax: obj << X( column(s) )

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );

Y

Syntax: obj << Y( column(s) )

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );

Item Messages

Change Variables

Syntax: obj << Change Variables

Description: Changes X, Y, and other variables for subsequent models.

JMP Version Added: 19

Compare

Syntax: obj << Compare

Description: Updates the XGBoost comparison metrics.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit( Objective( 0 ) ) );
obj << Compare( Correlation( 1 ) );

Fit

Syntax: obj << Fit

Description: Fits an XGBoost model. You can specify XGBoost parameters and fitting specifications within this.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );

Get Measures

Syntax: obj << Get Measures

JMP Version Added: 19

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.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
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.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
obj << Relaunch Analysis;

Show Details

Syntax: obj << Show Details( state=0|1 )

Description: Shows more details.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Show Details( 1 ) );

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.

JMP Version Added: 19

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: 19

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" ) );

Copy ByGroup Script

Syntax: obj << Copy ByGroup Script

Description: Create a JSL script to produce this analysis, and put it on the clipboard.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
obj[1] << Copy ByGroup Script;

Copy Script

Syntax: obj << Copy Script

Description: Create a JSL script to produce this analysis, and put it on the clipboard.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
obj << Copy Script;

Get By Levels

Syntax: obj << Get By Levels

Description: Returns an associative array mapping the by group columns to their values.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
biv = dt << Bivariate( X( :height ), Y( :weight ), By( :sex ) );
biv << Get By Levels;

Get ByGroup Script

Syntax: obj << Get ByGroup Script

Description: Creates a script (JSL) to produce this analysis and returns it as an expression.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
t = obj[1] << Get ByGroup Script;
Show( t );

Get Container

Syntax: obj << Get Container

Description: Returns a reference to the container box that holds the content for the object.

JMP Version Added: 19

General

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
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.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
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.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
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.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
t = obj << Get Script With Data Table;
Show( t );

Get Timing

Syntax: obj << Get Timing

Description: Times the platform launch.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
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.

JMP Version Added: 19

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: 19

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.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << Bivariate(
    Ignore Platform Preferences( 1 ),
    Y( :height ),
    X( :weight ),
    Action( Distribution( Y( :height, :weight ), Histograms Only ) )
);

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: 19

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: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Oneway( Y( :height ), X( :sex ), t Test( 1 ) );
preset = obj << New Preset();

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: 19

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.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
r = obj << Report;
t = r[Outline Box( 1 )] << Get Title;
Show( t );

Save ByGroup Script to Data Table

Syntax: Save ByGroup Script to Data Table( <name>, < <<Append Suffix(0|1)>, < <<Prompt(0|1)>, < <<Replace(0|1)> );

Description: Creates a JSL script to produce this analysis, and save it as a table property in the data table. You can specify a name for the script. The Append Suffix option appends a numeric suffix to the script name, which differentiates the script from an existing script with the same name. The Prompt option prompts the user to specify a script name. The Replace option replaces an existing script with the same name.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
obj[1] << Save ByGroup Script to Data Table;

Save ByGroup Script to Journal

Syntax: obj << Save ByGroup Script to Journal

Description: Create a JSL script to produce this analysis, and add a Button to the journal containing this script.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
obj[1] << Save ByGroup Script to Journal;

Save ByGroup Script to Script Window

Syntax: obj << Save ByGroup Script to Script Window

Description: Create a JSL script to produce this analysis, and append it to the current Script text window.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
obj[1] << Save ByGroup Script to Script Window;

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.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
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.

JMP Version Added: 19

Example 1

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
obj[1] << Save Script for All Objects To Data Table;

Example 2

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
dt << New Column( "_bycol",
    Character,
    Nominal,
    set values( Repeat( {"A", "B"}, N Rows( dt ) )[1 :: N Rows( dt )] )
);
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
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.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
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.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
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.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
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.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
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.

JMP Version Added: 19

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.

JMP Version Added: 19

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.

JMP Version Added: 19

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 )} ) )
);

Title

Syntax: obj << Title( "new title" )

Description: Sets the title of the platform.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
obj << Title( "My Platform" );

Top Report

Syntax: obj << Top Report

Description: Returns a reference to the root node in the report.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = XGBoost( Y( :Weight ), X( :Height ), Fit );
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.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
obj = dt << Bivariate( Y( :Weight ), X( :Height ) );
xml = obj << View Web XML;

XGBoost Compare

Associated Constructors

XGBoost Compare

Syntax: XGBoost Compare

JMP Version Added: 19

Item Messages

AUC

Syntax: obj << AUC( state=0|1 )

Description: Shows or hides the AUROC, which is the area under the receiver operating characteristic curve. On by default.

JMP Version Added: 19

AUPRC

Syntax: obj << AUPRC( state=0|1 )

Description: Area under the Precision Recall Curve On by default.

JMP Version Added: 19

Accuracy

Syntax: obj << Accuracy( state=0|1 )

Description: Shows or hides the accuracy, which is the proportion of correct classifications. On by default.

JMP Version Added: 19

Censor

Syntax: obj << Censor( state=0|1 )

Description: Shows or hides the Censor command On by default.

JMP Version Added: 19

Concordance

Syntax: obj << Concordance( state=0|1 )

Description: Shows or hides the concordance, which is the Harrell C-Index and measures strength of sorting efficiency On by default.

JMP Version Added: 19

Correlation

Syntax: obj << Correlation( state=0|1 )

Description: Shows or hides the Pearson correlation, which is a measure of the strength of the linear relationship. On by default.

JMP Version Added: 19

F1

Syntax: obj << F1( state=0|1 )

Description: Shows or hides the F1 Score, which is the harmonic average of precision and recall. On by default.

JMP Version Added: 19

Features

Syntax: obj << Features( state=0|1 )

Description: Shows or hides the Features column. On by default.

JMP Version Added: 19

Freq

Syntax: obj << Freq( state=0|1 )

Description: Shows or hides the Freq column. On by default.

JMP Version Added: 19

H Measure

Syntax: obj << H Measure( state=0|1 )

Description: Shows or hides the H Measure, which measures proportion improvement over baseline. On by default.

JMP Version Added: 19

Hide All Models

Syntax: obj << Hide All Models

Description: Hides all models.

JMP Version Added: 19

LogLoss

Syntax: obj << LogLoss( state=0|1 )

Description: Shows or hides the logarithm of the likelihood-based loss function. On by default.

JMP Version Added: 19

MAE

Syntax: obj << MAE( state=0|1 )

Description: Shows or hides the MAE, which is the mean absolute error. On by default.

JMP Version Added: 19

MCC

Syntax: obj << MCC( state=0|1 )

Description: Shows or hides the Matthews correlation coefficient, which is the Pearson correlation for binary variables. On by default.

JMP Version Added: 19

Misclass

Syntax: obj << Misclass( state=0|1 )

Description: Shows or hides the misclassification rate, which is the proportion of incorrect classifications. On by default.

JMP Version Added: 19

Predictors

Syntax: obj << Predictors( state=0|1 )

Description: Shows or hides the Predictors column. On by default.

JMP Version Added: 19

Profit

Syntax: obj << Profit( state=0|1 )

Description: Shows or hides the expected profit. On by default.

JMP Version Added: 19

RMSE

Syntax: obj << RMSE( state=0|1 )

Description: Shows or hides the RMSE, which is the root mean square error. On by default.

JMP Version Added: 19

RSquare

Syntax: obj << RSquare( state=0|1 )

Description: Shows or hides RSquare value, which is the proportion of variability explained. On by default.

JMP Version Added: 19

Remove Hidden Models

Syntax: obj << Remove Hidden Models

Description: Removes all models for which the Show box is not checked.

JMP Version Added: 19

Remove Shown Models

Syntax: obj << Remove Shown Models

Description: Removes all models for which the Show check box is checked and shows the remaining models.

JMP Version Added: 19

Response

Syntax: obj << Response( state=0|1 )

Description: Shows or hides the Response column. On by default.

JMP Version Added: 19

Show All Models

Syntax: obj << Show All Models

Description: Shows all models.

JMP Version Added: 19

Training Metrics

Syntax: obj << Training Metrics( state=0|1 )

Description: Shows or hides all training metrics. On by default.

JMP Version Added: 19

Validation

Syntax: obj << Validation( state=0|1 )

Description: Shows or hides the Validation column. On by default.

JMP Version Added: 19

Validation Metrics

Syntax: obj << Validation Metrics( state=0|1 )

Description: Shows or hides all validation metrics. On by default.

JMP Version Added: 19

Weight

Syntax: obj << Weight( state=0|1 )

Description: Shows or hides the Weight column. On by default.

JMP Version Added: 19

XGBoost Fit

Associated Constructors

XGBoost Fit

Syntax: XGBoost Fit

JMP Version Added: 19

Item Messages

Actual by Predicted Plots

Syntax: obj << Actual by Predicted Plots( 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. On by default.

JMP Version Added: 19

Autotune

Syntax: obj << Autotune

Description: Creates a fast flexible filling design within min and max parameter settings to fit n models, where n is Number of Runs.

JMP Version Added: 19

Confusion Matrices

Syntax: obj << ( fit[number] << Confusion Matrices( state=0|1 ) )

Description: Shows or hides a crosstabulation matrix of actual and predicted levels. On by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << XGBoost(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit
);
obj << (fit[1] << Confusion Matrices( 1 ));

Contour Profiler

Syntax: obj << Contour Profiler

Description: Shows or hides interactive graphs of cross-sections of the prediction function.

JMP Version Added: 19

Copy Parameters to Launch

Syntax: obj << Copy Parameters to Launch

Description: Copies the parameters from this model to the model launch section.

JMP Version Added: 19

Decision Thresholds

Syntax: obj << Decision Thresholds( state=0|1 )

Description: Shows or hides decision threshold graphs and tables. On by default.

JMP Version Added: 19

Fit Details

Syntax: obj << Fit Details( state=0|1 )

Description: Shows or hides the statistics for the fitted model. On by default.

JMP Version Added: 19

Generate Python Code

Syntax: obj << Generate Python Code

Description: Creates Python code for training and scoring.

JMP Version Added: 19

Importances

Syntax: obj << Importances( state=0|1 )

Description: Shows or hides the importance statistics for each predictor. On by default.

JMP Version Added: 19

Lift Curves

Syntax: obj << Lift Curves( 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.

JMP Version Added: 19

Number of Design Points

Syntax: obj << Number of Design Points( number=10 )

Description: Specifies the number of tuning design runs to perform. If you have a large problem, keep this value relatively small. "10" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( Number of Design Points( 10 ) ) );

Number of Inner Folds

Syntax: obj << Number of Inner Folds( number=2 )

Description: Specifies the number of nested inner folds used during the autotune process. "2" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( Number of Inner Folds( 2 ) ) );

Objective

Syntax: obj << Objective( "reg:squarederror"|"binary:logistic"|"binary:hinge"|"count:poisson"|"multi:softprob"|"rank:pairwise"|"rank:ndcg"|"rank:map"|"reg:gamma"|"reg:logistic"|"reg:pseudohubererror"|"reg:squaredlogerror"|"reg:tweedie"|"survival:cox"="reg:squarederror" )

Description: Specifies the function to be optimized for model fitting. The function must be consistent with the modeling type of the response. "reg:squarederror" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), objective( "reg:squarederror" ) );

Precision Recall Curves

Syntax: obj << Precision Recall Curves( state=0|1 )

Description: Plots the trade-off between precision and recall for different classification thresholds. It is preferred in scenarios where class imbalances exist.

JMP Version Added: 19

Profiler

Syntax: obj << Profiler

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.

JMP Version Added: 19

Publish Prediction Formula

Syntax: obj << Publish Prediction Formula

Description: Creates prediction formulas and saves them as formula column scripts in the Formula Depot platform.

JMP Version Added: 19

ROC Curves

Syntax: obj << ROC Curves( 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).

JMP Version Added: 19

Remove All But This Fit

Syntax: obj << ( fit[number] << Remove All But This Fit )

Description: Removes the reports and plots for all models except this one.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << XGBoost(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit
);
Wait( 2 );
obj << (Fit[1] << Remove All But This Fit);

Remove Fit

Syntax: obj << ( fit[number] << Remove Fit )

Description: Removes the entire model report.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Iris.jmp" );
obj = dt << XGBoost(
    Y( :Species ),
    X( :Sepal length, :Sepal width, :Petal length, :Petal width ),
    Fit
);
Wait( 2 );
obj << (Fit[1] << Remove Fit);

Save Predicteds

Syntax: obj << Save Predicteds

Description: Saves the predicted values in a new column in the data table.

JMP Version Added: 19

Save Prediction Formula

Syntax: obj << Save Prediction Formula

Description: Saves the prediction formula in a new column in the data table. Calculations can be slow for large models.

JMP Version Added: 19

Save SHAPs

Syntax: obj << Save SHAPs

Description: Saves Shapley values to the data table. These values break down predictions into components for each predictor.

JMP Version Added: 19

Surface Profiler

Syntax: obj << Surface Profiler

Description: Shows or hides interactive graphs of cross-sections of the prediction function.

JMP Version Added: 19

Tree Details

Syntax: obj << Tree Details( state=0|1 )

Description: Shows or hides the breakdown of each tree split.

JMP Version Added: 19

Tuning Design Table

Syntax: Tuning Design Table( "table name" )

Description: Specifies the name of an open JMP data table of parameter settings used to fit a series of models. The columns of this table must exactly match parameter names, and each row must contain values of these parameters to use for that model fit. Parameters that are not specified are set to their values from this dialog.

JMP Version Added: 19

alpha

Syntax: obj << alpha( number=0.0 )

Description: Specifies the L1 regularization term on weights. Increasing this value makes the model more conservative. This value must be nonnegative. "0.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( alpha( 0.0 ) ) );

alpha_max

Syntax: obj << alpha_max( number=0.5 )

Description: Specifies the maximum L1 regularization term on weights. Increasing this value makes the model more conservative. This value must be nonnegative. "0.5" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( alpha_max( 2.0 ) ) );

alpha_min

Syntax: obj << alpha_min( number=0.0 )

Description: Specifies the minimum L1 regularization term on weights. Increasing this value makes the model more conservative. This value must be nonnegative. "0.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( alpha_min( 0.0 ) ) );

base_score

Syntax: obj << base_score( number=0.5 )

Description: Specifies the initial prediction score of all instances, which is the global bias. The mean of y is typically a good choice. "0.5" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( base_score( 0.5 ) ) );

booster

Syntax: obj << booster( "gbtree"|"gblinear"|"dart"="gbtree" )

Description: Specifies which booster to use. "gbtree" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( booster( "gbtree" ) ) );

colsample_bylevel

Syntax: obj << colsample_bylevel( number=1.0 )

Description: Specifies the proportion of columns to sample for each level. Sampling occurs once for every new depth level reached in a tree. "1.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( colsample_bylevel( 1.0 ) ) );

colsample_bynode

Syntax: obj << colsample_bynode( number=1.0 )

Description: Specifies the proportion of columns to sample for each node (split). Sampling occurs once every time a new split is evaluated. "1.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( colsample_bynode( 1.0 ) ) );

colsample_bytree

Syntax: obj << colsample_bytree( number=1.0 )

Description: Specifies the proportion of columns to sample when constructing each tree. Sampling occurs once for each tree. This value must be between 0 and 1. "1.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( colsample_bytree( 1.0 ) ) );

colsample_bytree_max

Syntax: obj << colsample_bytree_max( number=1.0 )

Description: Specifies the maximum proportion of columns to sample when constructing each tree. Sampling occurs once for each tree. This value must be between 0 and 1. "1.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( colsample_bytree_max( 1.0 ) ) );

colsample_bytree_min

Syntax: obj << colsample_bytree_min( number=0.5 )

Description: Specifies the minimum proportion of columns to sample when constructing each tree. Sampling occurs once for each tree. This value must be between 0 and 1. "0.5" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( colsample_bytree_min( 0.3 ) ) );

eval_metric

Syntax: obj << eval_metric( text )

Description: Specifies the metric shown in the iteration history plot but does not affect the actual model fit. Leave this value blank for the default metric corresponding to the objective function, or specify one of the following: rmse, rmsle, mae, logloss, error, error@t, merror, auc, aucpr, ndcg, map, ndcg@n, map@n, ndcg-, map-, ndcg@n-, map@n-, poisson-nloglik, gamma-nloglik, cox-nloglik, gamma-deviance, tweedie-nloglik.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( eval_metric( rmse ) ) );

feature_selector

Syntax: obj << feature_selector( "cyclic"|"shuffle"|"greedy"|"thrifty"="cyclic" )

Description: Specifies the feature selection and ordering method for the linear booster. "cyclic" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost(
    Y( :Weight ),
    X( :Height ),
    Booster( "gblinear" ),
    Fit( feature_selector( "cyclic" ) )
);

gamma

Syntax: obj << gamma( number=0.0 )

Description: Specifies the minimum loss reduction required to make a further partition on a leaf node of the tree. "0.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( Gamma( 0.0 ) ) );

grow_policy

Syntax: obj << grow_policy( "depthwise"|"lossguide"="depthwise" )

Description: Specifies the method used to add new nodes to trees. Currently, this option applies only when tree_method=hist. "depthwise" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( grow_policy( "depthwise" ) ) );

interaction_constraints

Syntax: obj << interaction_constraints( text )

Description: Specifies feature interaction constraints as a nested list of feature indices using brackets. Features grouped together can interact only with each other.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
:Age << Set Modeling Type( "Continuous" );
XGBoost( Y( :Weight ), X( :Age, :Height ), Fit( interaction_constraints( "[[0,1]]" ) ) );

iterations

Syntax: obj << iterations( number=30 )

Description: Specifies the number of boosting iterations. "30" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( iterations( 100 ) ) );

iterations_max

Syntax: obj << iterations_max( number=100 )

Description: Specifies the maximum number of boosting iterations. "100" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( iterations_max( 300 ) ) );

iterations_min

Syntax: obj << iterations_min( number=20 )

Description: Specifies the minimum number of boosting iterations. "20" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( iterations_min( 20 ) ) );

lambda

Syntax: obj << lambda( number=1.0 )

Description: Specifies the L2 regularization term on weights. Increasing this value makes the model more conservative. This value must be nonnegative. "1.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( lambda( 1.0 ) ) );

lambda_max

Syntax: obj << lambda_max( number=2.0 )

Description: Specifies the maximum L2 regularization term on weights. Increasing this value makes the model more conservative. This value must be nonnegative. "2.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( lambda_max( 2.0 ) ) );

lambda_min

Syntax: obj << lambda_min( number=0.0 )

Description: Specifies the minimum L2 regularization term on weights. Increasing this value makes the model more conservative. This value must be nonnegative. "0.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( lambda_min( 0.0 ) ) );

learning_rate

Syntax: obj << learning_rate( number=0.3 )

Description: Specifies the learning rate. Smaller learning rates tend to fit better but require more iterations to converge, whereas larger learning rates fit faster. "0.3" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( learning_rate( 0.3 ) ) );

learning_rate_max

Syntax: obj << learning_rate_max( number=0.4 )

Description: Specifies the maximum learning rate. Smaller learning rates tend to fit better but require more iterations to converge, whereas larger learning rates fit faster. "0.4" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( learning_rate_max( 0.4 ) ) );

learning_rate_min

Syntax: obj << learning_rate_min( number=0.05 )

Description: Specifies the minimum learning rate. Smaller learning rates tend to fit better but require more iterations to converge, whereas larger learning rates fit faster. "0.05" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( learning_rate_min( 0.05 ) ) );

max_bin

Syntax: obj << max_bin( number=256 )

Description: Specifies the maximum number of discrete bins into which to bucket continuous features. This option applies only for tree_method=hist. "256" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( max_bin( 256 ) ) );

max_delta_step

Syntax: obj << max_delta_step( number=0.0 )

Description: Specifies the maximum delta step that each leaf output can take. "0.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( max_delta_step( 0.0 ) ) );

max_depth

Syntax: obj << max_depth( number=6 )

Description: Specifies the maximum depth of the tree. This value must be an integer. Complexity increases as depth increases. Models with larger max_depth have a higher risk of overfitting. "6" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( max_depth( 6 ) ) );

max_depth_max

Syntax: obj << max_depth_max( number=8 )

Description: Specifies the maximum depth of tree maximum. This value must be an integer. Complexity increases as depth increases. Models with depths of 2^depth and larger have higher risk of overfitting. "8" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( max_depth_max( 9 ) ) );

max_depth_min

Syntax: obj << max_depth_min( number=1 )

Description: Specifies the maximum depth of tree minimum. This value must be an integer. Complexity increases as depth increases. Models with depths of 2^depth and larger have higher risk of overfitting. "1" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( max_depth_min( 3 ) ) );

max_leaves

Syntax: obj << max_leaves( number=0 )

Description: Specifies the maximum number of nodes to be added. This option applies only for grow_policy=lossguide. "0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( max_leaves( 0 ) ) );

min_child_weight

Syntax: obj << min_child_weight( number=1.0 )

Description: Specifies the minimum sum of instance weight (Hessian) needed in a child. This value is the minimum size of each leaf. "1.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( min_child_weight( 1.0 ) ) );

min_child_weight_max

Syntax: obj << min_child_weight_max( number=3.0 )

Description: Specifies the maximum sum of instance weight (Hessian) needed in a child. This value is the maximum size of each leaf. "3.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( min_child_weight_max( 10.0 ) ) );

min_child_weight_min

Syntax: obj << min_child_weight_min( number=1.0 )

Description: Specifies the minimum sum of instance weight (Hessian) needed in a child. This value is the minimum size of each leaf. "1.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( min_child_weight_min( 1.0 ) ) );

monotone_constraints

Syntax: obj << monotone_constraints( text )

Description: Specifies monotonicity constraints for each feature. The constraints must be specified using a comma-separated list of values within parentheses, where -1 indicates negative, 1 indicates positive, and 0 indicates no constraint.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
:Age << Set Modeling Type( "Continuous" );
XGBoost( Y( :Weight ), X( :Age, :Height ), Fit( monotone_constraints( "(1,1)" ) ) );

normalize_type

Syntax: obj << normalize_type( "tree"|"forest"="tree" )

Description: Specifies the type of normalization algorithm for the DART booster. "tree" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Booster( "dart" ), Fit( normalize_type( "tree" ) ) );

nthread

Syntax: obj << nthread( number=0 )

Description: Specifies the number of parallel threads used to run XGBoost. By default, all available threads are used. "0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( nthread( 8 ) ) );

num_parallel_tree

Syntax: obj << num_parallel_tree( number=1 )

Description: Specifies the number of boosted trees to grow in parallel. Then the results are averaged. "1" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( num_parallel_tree( 1 ) ) );

one_drop

Syntax: obj << one_drop( number=0 )

Description: When this flag is enabled in the DART booster, at least one tree is always dropped during the dropout. "0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( booster( "dart" ), one_drop( 0 ) ) );

predictor

Syntax: obj << predictor( "auto"|"cpu_predictor"|"gpu_predictor"="auto" )

Description: Specifies the type of predictor algorithm. "auto" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( predictor( "cpu_predictor" ) ) );

process_type

Syntax: obj << process_type( "default"|"update"="default" )

Description: Specifies the type of boosting process to run. "default" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( process_type( "default" ) ) );

rate_drop

Syntax: obj << rate_drop( number=0.0 )

Description: Specifies the dropout rate for the DART booster. "0.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( booster( "dart" ), rate_drop( 0.0 ) ) );

refresh_leaf

Syntax: obj << refresh_leaf( number=1 )

Description: Specifies the parameter of the refresh updater. If set to 1, leaves and nodes updated. If set to 0, only nodes are updated. "1" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( refresh_leaf( 1 ) ) );

sample_type

Syntax: obj << sample_type( "uniform"|"weighted"="uniform" )

Description: Specifies the type of sampling algorithm for the DART booster. "uniform" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Booster( "dart" ), Fit( sample_type( "uniform" ) ) );

scale_pos_weight

Syntax: obj << scale_pos_weight( number=1.0 )

Description: Specifies the balance of positive and negative weights, which are useful for unbalanced classes. A typical value to consider is sum(negative instances) / sum(positive instances). "1.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( scale_posweight( 1.0 ) ) );

seed

Syntax: obj << seed( number=0 )

Description: Specifies the seed for the random number generator. Set this value for reproducibility of the results. "0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( seed( 0 ) ) );

sketch_eps

Syntax: obj << sketch_eps( number=0.03 )

Description: Used only for tree_method=approx, this value approximately translates into (1 / sketch_eps) = number of bins. "0.03" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( sketch_eps( 0.03 ) ) );

skip_drop

Syntax: obj << skip_drop( number=0.0 )

Description: Specifies the probability of skipping the dropout procedure during a DART boosting iteration. "0.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( booster( "dart" ), skip_drop( 0.0 ) ) );

subsample

Syntax: obj << subsample( number=1.0 )

Description: Specifies the proportion of rows to sample during each iteration. This value must be between 0 and 1. This is a type of bagging. "1.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( subsample( 1.0 ) ) );

subsample_max

Syntax: obj << subsample_max( number=1.0 )

Description: Specifies the maximum proportion of rows to sample during each iteration. This value must be between 0 and 1. This is a type of bagging. "1.0" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( subsample_max( 1.0 ) ) );

subsample_min

Syntax: obj << subsample_min( number=0.5 )

Description: Specifies the minimum proportion of rows to sample during each iteration. This value must be between 0 and 1. This is a type of bagging. "0.5" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( subsample_min( 0.3 ) ) );

top_k

Syntax: obj << top_k( number=256 )

Description: Specifies the number of top features to select in greedy and thrifty feature selector. This option applies only for the gblinear booster. "256" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( booster( "gblinear" ), top_k( 0 ) ) );

tree_method

Syntax: obj << tree_method( "auto"|"exact"|"approx"|"hist"|"gpu_exact"|"gpu_hist"="auto" )

Description: Specifies the tree construction algorithm used in XGBoost. "auto" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( tree_method( "auto" ) ) );

tweedie_variance_power

Syntax: obj << tweedie_variance_power( number=1.5 )

Description: Specifies the power of the Tweedie distribution. This value must be between 1 and 2. This option applies only for objective=reg:tweedie. "1.5" by default.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost(
    Y( :Weight ),
    X( :Height ),
    Fit( objective( "reg:tweedie" ), tweedie_variance_power( 1.5 ) )
);

updater

Syntax: obj << updater( text )

Description: Specifies the tree updater to run for the gbtree booster. Specify one of the following: grow_colmaker, distcol, grow_histmaker, grow_local_histmaker, grow_skmaker, sync, refresh, prune. For the gblinear booster, specify either shotgun or coord_descent.

JMP Version Added: 19

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );
XGBoost( Y( :Weight ), X( :Height ), Fit( updater( "grow_colmaker" ) ) );