XGBoost is the new algorithm developed in 2014 by Tianqi Chen based on the Gradient boosting principles. It has created a storm in the data science community since its inception. XGBoost has been developed with both deep consideration in terms of system optimization and principles in machine learning. The goal of the library is to push the extremes of the computation limits of machines to provide scalable, portable, and accurate results:
# Xgboost Classifier >>> import xgboost as xgb >>> xgb_fit = xgb.XGBClassifier(max_depth=2, n_estimators=5000, learning_rate=0.05) >>> xgb_fit.fit(x_train, y_train) >>> print ("\nXGBoost - Train Confusion Matrix\n\n",pd.crosstab(y_train, xgb_fit.predict(x_train),rownames = ["Actuall"],colnames = ["Predicted"])) >>> print ("\nXGBoost - Train accuracy",round(accuracy_score(y_train, xgb_fit.predict(x_train)),3)) >>> print ("\nXGBoost - Train Classification...
