Why is correlation between my independent variables helping my linear regression model?

I am working with PUBG data and developing a linear regression model for the same ! Now there were three features in my original dataset, ridedistance, swimdistance, walkdistance. I combined the three with a new feature : distance covered which is the sum of the above mentioned three features. When putting it in a linearregression model, when I use the three features and the fourth one as well, I get a better score as compared to using the three featues only or using only the fourth feature. I have read that correlation between features when developing a model should not be there. But when all features (4 of them) having correlation are used to develop a model, the model has a better square (R-square). Why is this happening ?

• It would be better if you specify the model...is it a NN linear regression model? or simply a ML regression model? Also what are you trying to predict? Jan 27, 2019 at 16:49
• @DuttaA It is a linear regression model from scikit-learn. There are 29 total columns (all continuous) and I am trying to predict a float number from 0 to 1 which can have any value upto 2 places of decimal. Jan 27, 2019 at 16:50
• I don't know what sickit-learn is using, so you need to specify what exactly are you using...whether it is a NN based model or not.. Jan 27, 2019 at 16:52
• It;s not NN based model. It's a model based on gradient descent and regression much like y_predict = m1x1 + m2x2 + ... + c Jan 27, 2019 at 16:58
• I am guessing it is an overfit model. Also, I doubt if there will be collinearity among the first 3 - the collinearity would exist between the each of the first 3 with the fourth variable. Feb 22, 2020 at 13:29