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Feature selection helps with figuring out what features are the most important or the most relevant to use for a particular model. When two features are highly correlated, they will have an impact on the logistic regression, it will increase the standard errors of the coefficients.
The stronger the correlation, the more difficult it is to change one variable without changing another. It becomes difficult for the model to estimate the relationship between each independent variable and the dependent variable independently because the independent variables tend to change in unison.
Is there a correlation between a feature and a feature?
No Correlation: No relationship between those two attributes. Each of those correlation types can exist in a spectrum represented by values from 0 to 1 where slightly or highly positive correlation features can be something like 0.5 or 0.7.
I read somewhere that if we have features that are too correlated, we have to remove one, as this may worsen the model. It is clear that correlated features means that they bring the same information, so it is logical to remove one of them. But I can not understand why this can worsen the model.
Why does feature correlation matter in multiple regression?
If your dataset has perfectly positive or negative attributes then there is a high chance that the performance of the model will be impacted by a problem called — “Multicollinearity”. Multicollinearity happens when one predictor variable in a multiple regression model can be linearly predicted from the others with a high degree of accuracy.
Is the effect of having correlated predictors significant?
It is often the case that two (or more) variables will be correlated and both related to the dependent variable. Whether they are significant or not depends on both effect size and cell size.