Difference Between Ridge Regression Vs Lasso Regression

Ridge regression versus lasso regression

Aspect	Ridge Regression	Lasso Regression
Regularization Approach	Adds penalty term proportional to square of coefficients	Adds penalty term proportional to absolute value of coefficients
Coefficient Shrinkage	Coefficients shrink towards but never exactly to zero	Some coefficients can be reduced exactly to zero
Effect on Model Complexity	Reduces model complexity and multicollinearity	Results in simpler, more interpretable models
Handling Correlated Inputs	Handles correlated inputs effectively	Can be inconsistent with highly correlated features
Feature Selection Capability	Limited	Performs feature selection by reducing some coefficients to zero
Preferred Usage Scenarios	All features assumed relevant or dataset has multicollinearity	When parsimony is advantageous, especially in high-dimensional datasets
Decision Factors	Nature of data, desired model complexity, multicollinearity	Nature of data, desire for feature selection, potential inconsistency with correlated features
Selection Process	Often determined through cross-validation	Often determined through cross-validation and comparative model performance assessment

Ridge Regression in Machine Learning

Ridge regression is a key technique in machine learning, indispensable for creating robust models in scenarios prone to overfitting and multicollinearity. This method modifies standard linear regression by introducing a penalty term proportional to the square of the coefficients, which proves particularly useful when dealing with highly correlated independent variables. Among its primary benefits, ridge regression effectively reduces overfitting through added complexity penalties, manages multicollinearity by balancing effects among correlated variables, and enhances model generalization to improve performance on unseen data.

The implementation of ridge regression in practical settings involves the crucial step of selecting the right regularization parameter, commonly known as lambda. This selection, typically done using cross-validation techniques, is vital for balancing the bias-variance tradeoff inherent in model training. Ridge regression enjoys widespread support across various machine learning libraries, with Python’s scikit-learn being a notable example. Here, implementation entails defining the model, setting the lambda value, and employing built-in functions for fitting and predictions. Its utility is particularly notable in sectors like finance and healthcare analytics, where precise predictions and robust model construction are paramount. Ultimately, ridge regression’s capacity to improve accuracy and handle complex data sets solidifies its ongoing importance in the dynamic field of machine learning.

Beta coefficient impact.

The higher the value of the beta coefficient, the higher is the impact.

1. Dishes like Rice Bowl, Pizza, Desert with a facility like home delivery and website_homepage_mention plays an important role in demand or number of orders being placed in high frequency.

2. Variables showing negative effect on regression model for predicting restaurant orders: cuisine_Indian,food_category_Soup , food_category_Pasta , food_category_Other_Snacks.

3. Final_price has a negative effect on the order – as expected.

4. Dishes like Soup, Pasta, other_snacks, Indian food categories hurt model prediction on the number of orders being placed at restaurants, keeping all other predictors constant.

Difference Between Ridge Regression Vs Lasso Regression