plotVariableImportance#

Purpose#

Plots variable importance table after decision forest regression.

Format#

plotVariableImportance(dfm)#

Parameters:: dfm (Struct) – A filled instance of the dfModel structure.

Examples#

new;
library gml;
rndseed 23423;

/*
** Load data and prepare data
*/
// Load hitters dataset
dataset = getGAUSSHome("pkgs/gml/examples/hitters.xlsx");

// Load data from dataset and split
// into (70%) training and (30%) test sets
{ y_train, y_test, X_train, X_test } = trainTestSplit(dataset, "ln(salary)~ AtBat + Hits + HmRun + Runs + RBI + Walks + Years + PutOuts + Assists + Errors", 0.7);

/*
** Train model
*/
// Structure to hold trained model
struct dfModel out;

// Use constrol structure for settings
struct dfControl dfc;
dfc = dfControlCreate();

// Turn on variable importance
dfc.variableImportanceMethod = 1;

// Fit training data using decision forest
out = decForestRFit(y_train, X_train, dfc);

/*
** Set up plot of variable importance
*/
plotVariableImportance(out);