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XGBoost for Poisson Regression

XGBoost can be used to fit Poisson regression models for predicting count data.

Poisson regression is a generalized linear model that’s useful when the target variable represents counts, such as the number of events occurring in a fixed interval of time.

Here’s a quick example of how to train an XGBoost model for Poisson regression using the scikit-learn API.

# XGBoosting.com
# Fit an XGBoost Model for Poisson Regression using scikit-learn API
from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
import numpy as np

# Generate a synthetic dataset suitable for Poisson regression
X, y = make_regression(n_samples=1000, n_features=5, noise=10, random_state=42)
y = np.random.poisson(np.exp(y / 25))

# Initialize XGBRegressor
model = XGBRegressor(objective='count:poisson', random_state=42)

# Split data into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Fit the model on the training data
model.fit(X_train, y_train)

# Make predictions on the test set
predictions = model.predict(X_test)

The key steps:

  1. Initialize an XGBRegressor with the appropriate objective (here, 'count:poisson' for Poisson regression).
  2. Split your data into training and testing sets.
  3. Fit the model on the training data using fit().
  4. Make predictions on the test set using predict().

See Also