Neural

Example 1

Summary: Fit a neural network model for binary classification using specified input variables and a tanh activation function.

Code:

// Open data table
dt = Open("$Sample_Data/Diabetes.jmp");
// Neural of Y Binary
Neural(
    Y( :Y Binary ),
    X(
        :Age, :Gender, :BMI, :BP,
        :Total Cholesterol, :LDL, :HDL,
        :TCH, :LTG, :Glucose
    ),
    Validation( :Validation ),
    Informative Missing( 0 ),
    Set Random Seed( 1234 ),
    Fit( NTanH( 3 ) )
);

Example 2

Summary: Train a neural network model with ABRASION, MODULUS, ELONG, and HARDNESS as the response variables and SILICA, SILANE, and SULFUR as the predictors. Use Holdback validation with a holdback percentage of 33.33.

Code:

// Open data table
dt = Open("$Sample_Data/Tiretread.jmp");
// Neural
Neural(
    Y(
        :ABRASION, :MODULUS, :ELONG,
        :HARDNESS
    ),
    X( :SILICA, :SILANE, :SULFUR ),
    Informative Missing( 0 ),
    Validation Method(
        "Holdback", 0.3333
    ),
    Go,
    Profiler( 1 )
);