General question on EDA, correlations, classification, ML I am looking for a general best practices regarding classification and correlations. I created a new predictor feature call it B, based on a certain threshold in a feature A. Now I started to do EDA and I am not sure which feature to include in my EDA, A or B. When I do correlations plots, nothing correlates with feature B, but some features do correlate with feature A. Which one should I take into account then, A or B correlations? Also, how can I make use of those correlations and scatterplots and pairplots anyway and are they important? If I am using random forest or NN, do I even need to bother with all of the pairplots and correlations to extract features from? I have around 150 features and not sure how to approach the problem of which features to use. I haven't found a source saying how to make a proper use of all of this in a real world scenarios. Any help is appreciated.
If the relation between predictors is nearly 0, it's always better to drop that feature, the caveat here depends on the domain knowledge you have. 

Did you check the correlation between B and the target variable and also A and target variable? if it's negative drop it, If it's significantly high .i.e greater 0.7, use that as your feature.

Yes, pair plots and scatter plots are really important, but it would be tedious to plot features with 150 variables.