Add Ridge Regression to Machine Learning

[Maneeshbhaskarpulidindi]

Feature description

###Feature: Ridge Regression with Regularization

Description:

The program implements Ridge Regression, a type of linear regression that includes an L2 regularization term to prevent overfitting and improve generalization. It uses gradient descent to optimize the feature vector (weights) while adjusting for a regularization parameter (λλ). The program collects a dataset containing Average Damage per Round (ADR) and player ratings from a CSV file, processes the data, and fits a ridge regression model to find the best-fit line that predicts ADR based on player ratings.

Key Components:

• Data Collection:

• The program fetches a dataset from a remote CSV file containing player ratings and their corresponding Average Damage per Round (ADR).

• Feature Vector Initialization:

• Initializes a feature vector (θθ) to zero, representing the weights for the regression model.

• Gradient Descent Optimization:

• Implements steep gradient descent to update the feature vector based on the calculated gradients, ensuring convergence to the optimal weights.

• Regularization:

• Applies L2 regularization to the gradient updates to penalize larger weights, enhancing model robustness against overfitting.

• Error Calculation:

• Computes the sum of square errors to evaluate model performance and adjusts the feature vector iteratively.

• Mean Absolute Error Calculation:

• Adds a utility to compute mean absolute error between predicted and actual outcomes, providing insight into model accuracy.

-Result Output:

• Displays the resultant feature vector, which represents the optimized weights for predicting ADR based on player ratings.

Benefits:

• Improved Generalization: By incorporating regularization, the model can generalize better to unseen data
• Flexibility: Users can adjust the regularization parameter (λλ) to balance between fitting the training data and avoiding overfitting.

This feature enhances the program's capability to predict player performance metrics while providing a clear mechanism for controlling overfitting through regularization.

[imAkshayDarji]

Hello @Maneeshbhaskarpulidindi, I'm interested to doing this. Please assign it to me. Thanks!