# Linear and non-linear dependence in a single DS model

I have a dataset with parameters (features) a,b, c, etc. We need to develop a model to predict a (our target).

b is correlated with a significantly (85%) and I suspect linear dependence. c is a measurement of b in another depth, so it has a high correlation with b and a good correlation with a. Also, there are a bunch of other parameters (numerical features) whose dependence of a is non-linear (from background knowledge and box plot analysis). These features don't have a high correlation with a but have an effect on a.

I am building a model to predict a based on the knowledge above. I think that a multi-linear regression cannot be a good idea because of non-linear dependencies. On the other hand, b is a strong predictor of a and depends on a linearly. Therefore, I need to ensemble both linear regression and other methods (e.g., random forest) in a single model to have advantages of both.

• Since you need to map linear and non linear features to the target variable, I think you could try using a simple neural network to model this kind of a relationship. Sep 23 '20 at 16:32
• If some features cause non-linear dependence, maybe polynomial regression? Or some feature engineering to make relationship more linear. Sep 23 '20 at 18:03
• why a is in both place ? feature and response ? Sep 24 '20 at 12:29
• @user702846, It's just response (target). which sentence has confused you? Sep 24 '20 at 14:52
• @AmirCh so the response (target) is also a feature ? both are noted with 'a' Sep 25 '20 at 8:49