Random Forest Regression

This module contains an implementation of Random Forest Regression, an ensemble learning method that combines multiple decision trees to create a more robust and accurate model for predicting continuous outcomes based on input features.

Usage

To use Random Forest Regression, follow these steps:

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

Example:

from RandomForestRegression import RandomForestRegression

rfr_model = RandomForestRegression(n_trees=100, max_depth=5, max_features=2)
rfr_model.fit(X_train, y_train)
predictions = rfr_model.predict(X_test)

Parameters

• n_trees: Number of trees in the random forest.
• max_depth: Maximum depth of each decision tree.
• max_features: Maximum number of features to consider for each split.

Installation

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

pip install numpy

Coded By

Avdhesh Varshney

Happy Coding 👦