Machine Learning in Java - Second Edition

Evaluating models

Now, let's take a closer look at the evaluation function. The evaluation function accepts an initialized model, cross-validates the model on all three problems, and reports the results as an area under the ROC curve (AUC), as follows:

public static double[] evaluate(Classifier model)    throws Exception { 
 
  double results[] = new double[4]; 
 
  String[] labelFiles = new String[]{ 
    "churn", "appetency", "upselling"}; 
 
  double overallScore = 0.0; 
  for (int i = 0; i < labelFiles.length; i++) { 

First, we call the Instance loadData(String, String) function that we implemented earlier to load the training data and merge it with the selected labels:

 // Load data Instances train_data = loadData( path + "orange_small_train.data", path+"orange_small_train_"+labelFiles[i]+".labels.txt"); ...