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XGBoostMachine Learning
It is worth noting that an optimized implementation of Gradient Boosting is available in the popular Python library XGBoost, which stands for Extreme Gradient Boosting. This package was initially developed by Tianqi Chen as part of the Distributed (Deep) Machine Learning Community (DMLC), and it aims to be extremely fast, scalable, and portable. In fact, XGBoost is often an important component of winning entries in ML competitions. XGBoost’s API is quite similar to Scikit-Learn’s:
import xgboost
xgb_reg = xgboost.XGBRegressor()
xgb_reg.fit(X_train, y_train)
y_pred = xgb_reg.predict(X_val)
XGBoost also offers several nice features, such as automatically taking care of early stopping:
xgb_reg.fit(X_train, y_train,
eval_set=[(X_val, y_val)], early_stopping_rounds=2)
y_pred = xgb_reg.predict(X_val)
You should definitely check it out!
Some algorithms (such as SGD classifiers, Random Forest classifiers, and
naive Bayes classifiers) are capable of handling multiple classes natively.
Others (such as Logistic Regression or Support Vector Machine classifiers) are strictly binary classifiers. However, there are various strategies that you can use to perform multiclass classification with multiple binary classifiers.
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