Consider using per-class weights during training

Our classification problem is often unbalanced, with some classes having much more samples than other.

If we are interested in evaluating the problem in a way that each class gets equal weight (`macro` average) we could consider training the model using weights per class. In `sklearn`, this is achieved by passing an array `sample_weight` when calling
```python
model.fit(X, y, sample_weight)
```
where the `sample_weight` that gives more weight to under-represented classes can be easily computed using `sklearn`'s [`compute_sample_weight`](https://scikit-learn.org/stable/modules/generated/sklearn.utils.class_weight.compute_sample_weight.html).