Add threshold to predictions

performance.py

@@ -8,11 +8,10 @@ def evaluate(classifier, features, Y):
 
     print("Evaluating performance...")
 
-    #predictions = classifier.predict_proba(features)[:, 1]
-    #roc = roc_auc_score(Y, predictions)
-    #print("ROC of predictions: %s"%roc)
-
-    predictions = classifier.predict(features)
+    THRESHOLD = .3
+    
+    predictions_proba = classifier.predict_proba(features)[:, 1]
+    predictions = [1. if pred>THRESHOLD else 0. for pred in predictions_proba]
     cm = confusion_matrix(Y, predictions)
 
     Se = cm[1, 1] / float(cm[1, 1] + cm[1, 0])