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While working in regression analysis, you should be familiar with some very basic but very impactful concepts. In machine learning interviews, you can always expects questions from regression analysis. Regression analysis also develop the basic understanding of machine learning model building as we mostly start our machine learning journey from regression analysis only.
In R, stepAIC is one of the most commonly used search method for feature selection. We try to keep on minimizing the stepAIC value to come up with the final set of features. “stepAIC” does not necessarily means to improve the model performance, however it is used to simplify the model without impacting much on the performance. So AIC quantifies the amount of information loss due to this simplification. AIC stands for Akaike Information Criteria.
Linear Regression is a field of study which emphasizes on the statistical relationship between two continuous variables known as Predictor and Response variables. Predictor variable is most often denoted as x and also known as Independent variable. Response variable is most often denoted as y and also known as Dependent variable.
Covariance and Correlation are very helpful while understanding the relationship between two continuous variables. Covariance tells whether both variables vary in same direction (positive covariance) or in opposite direction (negative covariance). Whereas Correlation explains about the change in one variable leads how much proportion change in second variable.
In any business there are some easy to measure variables like : Age, Gender, Income, Education Level etc. and there are some difficult to measure
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