Support Vector Regression (SVR)

This module contains an implementation of Support Vector Regression (SVR), a regression technique using Support Vector Machines (SVM) principles.

Usage

To use Support Vector Regression, follow these steps:

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

Example:

from SupportVectorRegression import SupportVectorRegression

svr_model = SupportVectorRegression(epsilon=0.1, C=1.0)
svr_model.fit(X_train, y_train)
predictions = svr_model.predict(X_test)

Parameters

• epsilon: Epsilon in the epsilon-SVR model. It specifies the epsilon-tube within which no penalty is associated in the training loss function.
• C: Regularization parameter. The strength of the regularization is inversely proportional to C.

Installation

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

pip install numpy

Coded By

Avdhesh Varshney

Happy Coding 👦