8、Machine Learning: Random Forest, Gradient Boosting, and AdaBoost

Machine Learning: Random Forest, Gradient Boosting, and AdaBoost

1. Implementing a Random Forest in Python

Random forest can be implemented in Python using the scikit - learn library. Here are the implementation details:

from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier(n_estimators=100,max_depth=5,min_samples_leaf=100,random_state=10)
rfc.fit(X_train, y_train)

To make predictions:

rfc_pred=rfc.predict_proba(X_test)

After making predictions, the AUC can be calculated as follows:

from sklearn.metrics import roc_auc_score
roc_auc_score(y_test, rfc_pred[:,1])