Feature Correlation in Linear Regression

Requirements Clarification & Assessment

1. Understanding Linear Regression:

   • Linear regression is a statistical method used to model the relationship between a dependent variable and one or more independent variables by fitting a linear equation to observed data.

2. Feature Correlation:

   • Correlation between features refers to a statistical measure that expresses the extent to which two variables are linearly related.
   • High correlation between two features implies that they provide similar information about the variance in the dependent variable.

3. Multicollinearity:

   • Multicollinearity is a phenomenon where two or more predictor variables in a regression model are highly correlated, meaning that one can be linearly predicted from the others with a substantial degree of accuracy.
   • It violates the assumption that the predictors in a regression model should be independent of each other.

4. Impact on the Model:

   • The main concern is how multicollinearity affects the stability and interpretability of the regression model coefficients.
   • The requirement is to understand how correlation between features impacts model performance, interpretability, and statistical significance.