Understanding Multicollinearity and Confounding Variables in Regression

~ Harini Kannan ~ Leave a comment

Multicollinearity When two are more of the predictors are correlated, this phenomenon is called multicollinearity. This affects the resulting coefficients by masking the underlying individual weights of the correlated variables. This is why model weights are not equal to feature importance. Ways to deal with multicollinearity Looking at Variance Inflation Factor (VIf), which measures the … Continue reading Understanding Multicollinearity and Confounding Variables in Regression

2 minute refresher to Logistic Regression

~ Harini Kannan ~ Leave a comment

Here's a 2 minute refresher on Logistic regression for you: Logistic Regression is used to model the outcomes of a categorical target variable Input features are scaled just as with linear regression, however result is fed as an input to the logistic function. In linear regression, coefficients are found by minimizing the sum of squared … Continue reading 2 minute refresher to Logistic Regression