Gradient Boosting Regression

This module contains an implementation of Gradient Boosting Regression, an ensemble learning method that combines multiple weak learners (typically decision trees) to create a more robust and accurate model for predicting continuous outcomes based on input features.

Usage

To use Gradient Boosting Regression, follow these steps:

1. Import the GradientBoostingRegression class.
2. Create an instance of the class, specifying parameters such as the number of estimators, learning rate, and maximum depth.
3. Fit the model to your training data using the fit method.
4. Make predictions using the predict method.

Example:

from GradientBoostingRegression import GradientBoostingRegression

gbr_model = GradientBoostingRegression(n_estimators=100, learning_rate=0.1, max_depth=3)
gbr_model.fit(X_train, y_train)
predictions = gbr_model.predict(X_test)

Parameters

• n_estimators: Number of boosting stages (trees) to be run.
• learning_rate: Step size shrinkage to prevent overfitting.
• max_depth: Maximum depth of each decision tree.

Installation

To use this module, make sure you have the required dependencies installed:

pip install numpy

Coded By

Avdhesh Varshney

Happy Coding 👦