lift-1.py

#Create dataset
x,y = datasets.make_classification(n_samples=10000, n_features=20,
                                   n_informative=15,n_redundant=5,
                                   n_classes=2, weights=[0.95,0.05],
                                   random_state=1000)
#Split into Training and Test sets
x_train, x_test, y_train, y_test =  cross_validation.train_test_split(x,y,
                                                                      test_size=0.3,random_state=1000)
#Train a Decision Tree to Predict y                                                                      
clf = tree.DecisionTreeClassifier(min_samples_leaf=10)
clf.fit(x_train,y_train)