Decision Tree Regression

This module contains an implementation of Decision Tree Regression, a versatile algorithm for predicting a continuous outcome based on input features.

Usage

To use Decision Tree Regression, follow these steps:

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

Example:

from DecisionTreeRegression import DecisionTreeRegression

dt_model = DecisionTreeRegression(max_depth=3)
dt_model.fit(X_train, y_train)
predictions = dt_model.predict(X_test)

Parameters

• max_depth: Maximum depth of the decision tree. Controls the complexity of the model.

Installation

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

pip install numpy

Coded By

Avdhesh Varshney

Happy Coding 👦