The sklearn.ensemble.GradientBoostingRegressor is a class in the Python library scikit-learn (sklearn) that implements gradient boosting for regression problems. Gradient boosting is a machine learning technique that combines multiple weak predictive models (typically decision trees) to create a stronger and more accurate predictive model.
This class allows users to create and train a gradient boosting regressor model, which can be used to make predictions on new data. It offers flexibility in terms of specifying various hyperparameters such as the number of boosting stages, learning rate, and maximum tree depth.
The GradientBoostingRegressor class in sklearn also provides methods for fitting the model to training data, making predictions, and evaluating the performance of the model using different metrics. Overall, it is a powerful tool for solving regression problems and is widely used in various domains such as finance, healthcare, and marketing.
Python GradientBoostingRegressor - 60 examples found. These are the top rated real world Python examples of sklearn.ensemble.GradientBoostingRegressor extracted from open source projects. You can rate examples to help us improve the quality of examples.