how to manually give weight to features using python in machine learning

Question

I have a data set with continuous label ranging from one to five with nine different features. So I wanted to give weight to each feature manually because some of the features have very less dependency on the label so I wanted to give more weight to those features which have more dependency on the label. How can I manually give weight to each feature? Will it be possible to give weight like this?

I went through different documentations I can only find how to give weight to the label. What I only find is eliminating features ranking features etc. But I wanted to give weight to each feature manually also I wanted to tune these weights (Sometimes the feature weight will be different for different scenario so I wanted to tune the weight according to that)

Will it be possible ?

Answer 1

The whole point of using machine learning is to let it decide on its own how much weight should be given to which predictor based on its importance in predicting the label correctly.

It just doesn't makes any sense trying to do this on your own and then also use machine learning.

Answer 2

Don't assign weights manually, let the model learn the weights itself. It will automatically decide which features are more important.