Feature Engineering

You can take different combinations of features such as sum of features: feat_1 + feat_2 + feat_3..., or product of those. Or you can transform features by log, or exponential, sigmoid ... or even discretize the numeric feature into a categorical one. It's an infinite space to explore.

Whatever combination or transformation that increases your Cross-Validation or Test Set performance then you should use it.

How to Tune Gradient Boosting in Python

How to Tune RandomForestRegressor

min_leave_size = 50 to avoid capture noise

How to write a Python Module

__init__

print(sys.path)