title: How to understand sample_weight in sklearn.metrics?
body: <p>Do we need to set sample_weight when we evaluate our model? Now I have trained a model about classification, but the dataset is unbalanced. When I set the sample_weight with compute_sample_weight('balanced'), the scores are very nice. Precision:0.88, Recall:0.86 for '1' class.
  But the scores will be bad if I don't set the sample_weight. Precision:0.85, Recall:0.21.
  Will the sample_weight destroy the original data distribution? </p>