XGBoost Classifier

Create an XGBoost classifier object.

We'll cover the following

Chapter Goals:

  • Learn how to create a scikit-learn style classifier in XGBoost

A. Following the scikit-learn API

While XGBoost provides a more efficient model than scikit-learn, using the model can be a bit convoluted. For people who are used to scikit-learn, XGBoost provides wrapper APIs around its model for classification and regression. These wrapper APIs allow us to use XGBoost's efficient model in the same style as scikit-learn.

For classification, the XGBoost wrapper model is called XGBClassifier. Like regular scikit-learn models, it can be trained with a simple call to fit with NumPy arrays as input arguments.

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model = xgb.XGBClassifier(use_label_encoder=False, eval_metric='logloss')
# predefined data and labels
model.fit(data, labels)
# new_data contains 2 new data observations
predictions = model.predict(new_data)
print('Predictions:\n{}'.format(repr(predictions)))

Note that the predict function for XGBClassifier returns actual predictions (not probabilities).

All the parameters for the original Booster object are now keyword arguments for the XGBClassifier. For instance, we can specify the type of classification, i.e. the 'objective' parameter for Booster objects, with the objective keyword argument (the default is binary classification).

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model = xgb.XGBClassifier(objective='multi:softmax', eval_metric='mlogloss', use_label_encoder=False)
# predefined data and labels (multiclass dataset)
model.fit(data, labels)
# new_data contains 2 new data observations
predictions = model.predict(new_data)
print('Predictions:\n{}'.format(repr(predictions)))

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