import numpy as np
from sklearn import <model_name>

from sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score, r2_score
from sklearn.cross_validation import train_test_split

train_data, test_data, train_label, test_label = train_test_split(
    df['features'],
    df['label'],
    test_size=0.33,
    random_state=42
)

model = <model_name>(iterations=2, depth=2, learning_rate=1, loss_function='RMSE', logging_level='Verbose')

model.fit(train_data, train_label,)

preds_class = model.predict(train_data)

print(" accuracy = ", accuracy_score(preds_class, train_label))
print(" recall_score = ", recall_score(preds_class, train_label))
print(" precision_score = ", precision_score(preds_class, train_label))
print(" f1_score = ", f1_score(preds_class, train_label))