decForestPredict#

Purpose#

Predicts responses using the output from decForestCFit() or decForestRFit() and matrix of independent variables.

Format#

predictions = decForestPredict(dfm, x_test)#

Parameters:

dfm (struct) –

An instance of the dfModel structure filled by decForestRFit() or decForestCFit() and containing the following relevant members:

dfm.variableImportance	Matrix, 1 x p, variable importance measure if computation of variable importance is specified, zero otherwise.
dfm.oobError	Scalar, out-of-bag error if OOB error computation is specified, zero otherwise.
dfm.numClasses	Scalar, number of classes if classification model, zero otherwise.
dfm.opaqueModel	Matrix, contains model details for internal use only.

x_test (NxP matrix) – The test model features, or independent variables.

Returns:

predictions (Nx1 numeric or string vector) – The predictions.

Examples#

new;
library gml;

rndseed 23423;

// Create file name with full path
fname = getGAUSSHome("pkgs/gml/examples/breastcancer.csv");

// Load all variables from dataset, except for 'ID'
data = loadd(fname, ". -ID");

// Separate dependent and independent variables
y = data[., "class"];
X = delcols(data, "class");

// Split data into 70% training and 30% test set
{ y_train, y_test, x_train, x_test } = trainTestSplit(y, x, 0.7);

// Declare 'df_mdl' to be an 'dfModel' structure
// to hold the trained model
struct dfModel df_mdl;

// Train the decision forest classifier with default settings
df_mdl = decForestCFit(y_train, X_train);

// Make predictions on the test set, from our trained model
y_hat = decForestPredict(df_mdl, X_test);

// Print diagnostic report
call classificationMetrics(y_test, y_hat);

The code above will print the following output:

======================================================================
Model:              Decision Forest         Target variable:     class
Number Observations:            489         Number features:         9
Number of trees:                100           Obs. per Tree:      100%
Min. Obs. Per Node:               1     Impurity Threshhold:         0
======================================================================


======================================================================
Prediction Model:     DF Classification     Target variable:     class
Number Predictions:                 210     Number features:         9
======================================================================

===================================================
                             Classification metrics
===================================================
       Class   Precision  Recall  F1-score  Support

           0        0.99    0.99      0.99      154
           1        0.98    0.96      0.97       56

   Macro avg        0.98    0.98      0.98      210
Weighted avg        0.99    0.99      0.99      210

    Accuracy                          0.99      210