ANOVA feature selection

1. What is ANOVA in feature selection?
2. How does ANOVA help feature selection?
3. What are the features of ANOVA?

What is ANOVA in feature selection?

ANOVA f-test Feature Selection

ANOVA is an acronym for “analysis of variance” and is a parametric statistical hypothesis test for determining whether the means from two or more samples of data (often three or more) come from the same distribution or not.

How does ANOVA help feature selection?

ANOVA checks whether there is equal variance between groups of categorical feature with respect to the numerical response. If there is equal variance between groups, it means this feature has no impact on the response and hence it (the categorical variable) cannot be considered for model training.

What are the features of ANOVA?

In ANOVA, the dependent variable must be a continuous (interval or ratio) level of measurement. The independent variables in ANOVA must be categorical (nominal or ordinal) variables. Like the t-test, ANOVA is also a parametric test and has some assumptions. ANOVA assumes that the data is normally distributed.