2 things I would like to add to the already very good answers in this thread: > How do you treat colinearity? Look at feature interactions when using decision

2 things I would like to add to the already very good answers in this thread: > How do you treat colinearity? Look at feature interactions when using decision tree based models to discover if some interactions between seemingly similar features could improve the performance of the model. > How will you deal with unbalanced data where the ratio of negative and positive is huge? Don't use accuracy or other "averaging" evaluation metrics. When looking at unbalanced data you are likely interested in the minority class so I would use metrics that only look at this class for performance evaluation.