Description
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:
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Data Collection:
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The program fetches a dataset from a remote CSV file containing player ratings and their corresponding Average Damage per Round (ADR).
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Feature Vector Initialization:
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Initializes a feature vector (θθ) to zero, representing the weights for the regression model.
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Gradient Descent Optimization:
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Implements steep gradient descent to update the feature vector based on the calculated gradients, ensuring convergence to the optimal weights.
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Regularization:
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Applies L2 regularization to the gradient updates to penalize larger weights, enhancing model robustness against overfitting.
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Error Calculation:
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Computes the sum of square errors to evaluate model performance and adjusts the feature vector iteratively.
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Mean Absolute Error Calculation:
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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.