Machine Learning | Ridge Regression-L2

1. 1Apply L2 Regularization to a Linear Regression model using scikit-learn in Python
2. 2Configure the regularization strength parameter (alpha) to optimize model performance
3. 3Test the model on a dataset to evaluate its effectiveness in reducing overfitting
4. 4Compare the results with ordinary Linear Regression to see the improvement
5. 5Run cross-validation to ensure the model's performance is consistent across different datasets

Data scientists and machine learning engineers can benefit from this technique to improve model performance and prevent overfitting

💡 Ridge Regression with L2 Regularization reduces overfitting by adding a penalty term to the cost function

💡 Reduce overfitting in Linear Regression with Ridge Regression & L2 Regularization!