Use Cases and Applications of Ridge Regression
- Forecasting Economic Indicators: In other words, ridge regression can be applied to predict economic forecasts that can include growth in domestic products (GDP), inflation rates, unemployment rates, etc., using more than one predictor variable such as interest rates, consumer expenditure, industrial production, etc. When used, it can manage the phenomenon of multicollinearity among these predictors, making its forecasts more accurate.
- Medical Diagnosis: The use of ridge regression comes in scenarios where there are multiple biomarkers, or diagnostics, that are correlated in a medical diagnosis task. It assists in constructing well-anchored diagnostic models since it allows for the control of multicollinearity in the set biomarkers, thus improving disease diagnosis as well as prognosis.
- Sales Prediction: In marketing, ridge regression has wider applications to forecast sales with the help of different types of marketing, like advertisement cost, promotion, etc. It addresses issues of correlation between these marketing factors, thereby enabling marketers to better plan themselves and determine the best way to approach their sales targets.
- Climate Modeling: The climate modeling methodology: Ridge regression, as mentioned above, is useful for climate modeling with the help of multiple climate principles like temperature, precipitation, and pressure to predict future climate. It assists in the development of accurate models and eliminates interference between the climate variables, hence developing more accurate climate models.
- Risk Management: Ridge regression as used in credit scoring and other similar assignments in risk management. While including various financial ratios and characteristics of its customers, it is useful in evaluating the creditworthiness of the latter in cases of personal or business loans, addressing the issue of multicolinearity among the predictors, and thus improving the accuracy of risk analysis and management.
What is Ridge Regression?
Ridge regression, also known as Tikhonov regularization, is a technique used in linear regression to address the problem of multicollinearity among predictor variables. Multicollinearity occurs when independent variables in a regression model are highly correlated, which can lead to unreliable and unstable estimates of regression coefficients.
Ridge regression mitigates this issue by adding a regularization term to the ordinary least squares (OLS) objective function, which penalizes large coefficients and thus reduces their variance.
Table of Content
- What is Ridge Regression?
- Mathematical Formulation: Ridge Regression Estimator
- Bias-Variance Tradeoff for Ridge Regression
- Selection of the Ridge Parameter in Ridge Regression
- 1. Cross-Validation
- 2. Generalized Cross-Validation (GCV)
- 3. Information Criteria
- 4. Empirical Bayes Methods
- 5. Stability Selection
- Practical Considerations for Selecting Ridge Parameter
- Use Cases and Applications of Ridge Regression
- Advantages and Disadvantages of Ridge Regression