`WARNING: The least populated class in y has only 2 members, which is less than n_splits=3`

## Context & Description

When we run k-fold cross validation, we use `n_splits=3`.
https://github.com/BlueBrain/morphoclass/blob/021c632d2214ab25b5e4f58827f85420d536b66e/dvc/training/configs/splitter-stratifKFold.yaml#L4

But for layer `L4` and layer `L6` of the dataset `interneurons`, we have classes `L4_BP` and `L6_DBC` with `2<3` samples.

This situation generates the following Python warning when iterating over `StratifiedKFold.split(X, y)`:
```Python
UserWarning: The least populated class in y has only 2 members, which is less than n_splits=3.
```

## What happens?

If a class has less members than `n_splits`, then for some splits of `StratifiedKFold` we will have no representatives of that class in the validation or in the training set. For instance, splitting `[0] * 7 + [1] * 3` with `StratifiedKFold(n_splits=3)` yields the following training and validation sets, where in `split 3` there is no sample of class `1` in the validation set!
```sh
train-set --- valid-set
[1, 0, 0, 0, 0, 0] --- [1, 0, 0, 0] # split 1
[1, 0, 0, 0, 0, 0, 0] --- [1, 0, 0] # split 2
[1, 1, 0, 0, 0, 0, 0] --- [0, 0, 0] # split 3
```

## Why may this be an issue?
1. **Some metrics may not be computed and/or return wrong values.**
Metrics such as `precision_score`, `recall_score`, `f1_score` cannot be computed if there is no sample for a given class.
For instance, in the example above, using the validation set of `split 3` to compute `f1_score` for `y_pred = [0, 0, 0]` will return `0.0` (despite `y_pred` matching perfectly `y_true`!!) and raise this warning:
```python
UndefinedMetricWarning:
Precision is ill-defined and being set to 0.0 due to no predicted samples.
Use `zero_division` parameter to control this behavior.
```
**However** in our case we do not compute metrics per-split and then average across all splits, but instead we take all the out-of-sample predictions (generated during the various splits) and then compute the metric using all samples. Therefore, no class can ever have `0` samples during evaluation.

2. **A class in the validation set may not be present in the training set.**
This would be dramatic, because after the training the model would not even be aware the existence of a class that is however present in the validation set. So it is guaranteed that the model will never predict that class.

**However** I have never observed this happening on our data. I am not even sure it is possible.

3. **Evaluating on classes with few samples may be not very meaningful.**
Does it really make sense to take into account the model performance with respect to a class that has only 1 member in the training set or in the validation set?

**However** as long as we look at `micro` or `weighted` averages, the impact of (potentially awful) performance on classes with 1 or 2 samples is limited. But this could be bad if we want to look at `macro` averages. https://github.com/scikit-learn/scikit-learn/blob/baf828ca126bcb2c0ad813226963621cafe38adb/sklearn/metrics/_classification.py#L1049-L1062

## How do we solve this?

We could remove or merge classes with less than 3 samples.
But this should be discussed with the scientists. Maybe those classes with few samples are very important and well defined and must be kept anyway?