I was reading an article on "Credit Card Approval" using Logistic Regression, which basically tells us how Logistic Regression can be used to predict whether or not a credit card will be approved to a person or not. The features of this data set are found to be highly co-related with each other and while talking about why we should use logistic regression the author states that-->
Which model should we pick? A question to ask is: are the features that affect the credit card approval decision process correlated with each other? Although we can measure correlation, that is outside the scope of this notebook, so we'll rely on our intuition that they indeed are correlated for now. Because of this correlation, we'll take advantage of the fact that generalized linear models perform well in these cases. Let's start our machine learning modeling with a Logistic Regression model (a generalized linear model).
I am not able to understand why generalized linear models performs best for data with high correlation? Any help will be highly appreciated. Thank you. :)