X, y = X_transformed2, data.lpuntaje
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.1,
random_state=0)
clf = DecisionTreeRegressor(max_depth=6)
# Train Decision Tree Classifer
clf = clf.fit(X_train, y_train)
#Predict the response for test dataset
y_pred = clf.predict(X_test)
clf.score(X, y)
clf.get_depth()
clf.get_n_leaves()
clf.get_params()
# In[18]:
clf.get_depth()
# In[33]:
clf.score(X_test, y_test)
# In[31]:
# evaluate decision tree performance on train and test sets with different tree depths
from sklearn.datasets import make_classification
