Using the Scikit-Learn Estimator Interface



In addition to the native interface, XGBoost features a sklearn estimator interface that conforms to sklearn estimator guideline. It supports regression, classification, and learning to rank. Survival training for the sklearn estimator interface is still working in progress.

You can find some some quick start examples at Collection of examples for using sklearn interface. The main advantage of using sklearn interface is that it works with most of the utilities provided by sklearn like sklearn.model_selection.cross_validate(). Also, many other libraries recognize the sklearn estimator interface thanks to its popularity.

With the sklearn estimator interface, we can train a classification model with only a couple lines of Python code. Here’s an example for training a classification model:

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split

import xgboost as xgb

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, stratify=y, random_state=94)

# Use "hist" for constructing the trees, with early stopping enabled.
clf = xgb.XGBClassifier(tree_method="hist", early_stopping_rounds=2)
# Fit the model, test sets are used for early stopping., y_train, eval_set=[(X_test, y_test)])
# Save model into JSON format.

The tree_method parameter specifies the method to use for constructing the trees, and the early_stopping_rounds parameter enables early stopping. Early stopping can help prevent overfitting and save time during training.

Early Stopping

As demonstrated in the previous example, early stopping can be enabled by the parameter early_stopping_rounds. Alternatively, there’s a callback function that can be used xgboost.callback.EarlyStopping to specify more details about the behavior of early stopping, including whether XGBoost should return the best model instead of the full stack of trees:

early_stop = xgb.callback.EarlyStopping(
    rounds=2, metric_name='logloss', data_name='validation_0', save_best=True
clf = xgb.XGBClassifier(tree_method="hist", callbacks=[early_stop]), y_train, eval_set=[(X_test, y_test)])

At present, XGBoost doesn’t implement data spliting logic within the estimator and relies on the eval_set parameter of the method. If you want to use early stopping to prevent overfitting, you’ll need to manually split your data into training and testing sets using the sklearn.model_selection.train_test_split() function from the sklearn library. Some other machine learning algorithms, like those in sklearn, include early stopping as part of the estimator and may work with cross validation. However, using early stopping during cross validation may not be a perfect approach because it changes the model’s number of trees for each validation fold, leading to different model. A better approach is to retrain the model after cross validation using the best hyperparameters along with early stopping. If you want to experiment with idea of using cross validation with early stopping, here is a snippet to begin with:

from sklearn.base import clone
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import StratifiedKFold, cross_validate

import xgboost as xgb

X, y = load_breast_cancer(return_X_y=True)

def fit_and_score(estimator, X_train, X_test, y_train, y_test):
    """Fit the estimator on the train set and score it on both sets""", y_train, eval_set=[(X_test, y_test)])

    train_score = estimator.score(X_train, y_train)
    test_score = estimator.score(X_test, y_test)

    return estimator, train_score, test_score

cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=94)

clf = xgb.XGBClassifier(tree_method="hist", early_stopping_rounds=3)

results = {}

for train, test in cv.split(X, y):
    X_train = X[train]
    X_test = X[test]
    y_train = y[train]
    y_test = y[test]
    est, train_score, test_score = fit_and_score(
        clone(clf), X_train, X_test, y_train, y_test
    results[est] = (train_score, test_score)

Obtaining the native booster object

The sklearn estimator interface primarily facilitates training and doesn’t implement all features available in XGBoost. For instance, in order to have cached predictions, xgboost.DMatrix needs to be used with xgboost.Booster.predict(). One can obtain the booster object from the sklearn interface using xgboost.XGBModel.get_booster():

booster = clf.get_booster()


When early stopping is enabled, prediction functions including the xgboost.XGBModel.predict(), xgboost.XGBModel.score(), and xgboost.XGBModel.apply() methods will use the best model automatically. Meaning the xgboost.XGBModel.best_iteration is used to specify the range of trees used in prediction.

To have cached results for incremental prediction, please use the xgboost.Booster.predict() method instead.

Number of parallel threads

When working with XGBoost and other sklearn tools, you can specify how many threads you want to use by using the n_jobs parameter. By default, XGBoost uses all the available threads on your computer, which can lead to some interesting consequences when combined with other sklearn functions like sklearn.model_selection.cross_validate(). If both XGBoost and sklearn are set to use all threads, your computer may start to slow down significantly due to something called “thread thrashing”. To avoid this, you can simply set the n_jobs parameter for XGBoost to None (which uses all threads) and the n_jobs parameter for sklearn to 1. This way, both programs will be able to work together smoothly without causing any unnecessary computer strain.