XGBoost for Binary Classification

XGBoost can be used for binary classification tasks.

Binary classification involves predicting one of two classes. The output is typically modeled with a logistic function to return a probability.

Here’s a quick guide on how to fit an XGBoost model for binary classification using the scikit-learn API.

# XGBoosting.com
# Fit an XGBoost Model for Binary Classification using scikit-learn API
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier

# Generate a synthetic dataset with 2 classes
X, y = make_classification(n_samples=1000, n_features=20, n_informative=2, n_redundant=10, random_state=42)

# Initialize XGBClassifier
model = XGBClassifier(objective='binary:logistic', random_state=42)

# Fit the model to training data
model.fit(X, y)

# Make predictions with the fit model
predictions = model.predict(X)
print(predictions[:5])

In just a few lines of code, you can have a working XGBoost model:

Initialize an XGBClassifier with the appropriate objective (here, 'binary:logistic' for binary classification).
Fit the model to your training data using fit().

See Also