Nnetsauce

- Bayesian `CustomRegressor`
- Conformalized `CustomRegressor` (`splitconformal` and `localconformal` for now)
- See [this example](./examples/conformal_preds.py), [this example](./examples/custom_bayesian_regression.py), and [this notebook](./nnetsauce/demo/thierrymoudiki_20240317_conformal_regression.ipynb)

- `self.n_classes_ = len(np.unique(y))` for compatibility with sklearn

- `preprocess`ing for all `LazyDeep*`

- Attribute `estimators` (a list of `Estimator`'s as strings) for `LazyClassifier`,
`LazyRegressor`, `LazyDeepClassifier`, `LazyDeepRegressor`, `LazyMTS`, and `LazyDeepMTS`
- New documentation for the package, using `pdoc` (not `pdoc3`)
- Remove external regressors `xreg` at inference time for `MTS` and `DeepMTS`
- New class `Downloader`: querying the R universe API for datasets (see
https://thierrymoudiki.github.io/blog/2023/12/25/python/r/misc/mlsauce/runiverse-api2 for similar example in `mlsauce`)
- Add custom metric to `Lazy*`
- Rename Deep regressors and classifiers to `Deep*` in `Lazy*`
- Add attribute `sort_by` to `Lazy*` -- sort the data frame output by a given metric
- Add attribute `classes_` to classifiers (ensure consistency with sklearn)

- Subsample response by using the **number of rows**, not only a percentage (see [https://thierrymoudiki.github.io/blog/2024/01/22/python/nnetsauce-subsampling](https://thierrymoudiki.github.io/blog/2024/01/22/python/nnetsauce-subsampling))
- Improve consistency with sklearn's v1.2, for `OneHotEncoder`

- add **robust scaler**
- relatively **faster scaling** in preprocessing
- **Regression-based classifiers** (see [https://www.researchgate.net/publication/377227280_Regression-based_machine_learning_classifiers](https://www.researchgate.net/publication/377227280_Regression-based_machine_learning_classifiers))
- `DeepMTS` (multivariate time series forecasting with deep quasi-random layers): see https://thierrymoudiki.github.io/blog/2024/01/15/python/quasirandomizednn/forecasting/DeepMTS
- AutoML for `MTS` (multivariate time series forecasting): see https://thierrymoudiki.github.io/blog/2023/10/29/python/quasirandomizednn/MTS-LazyPredict
- AutoML for `DeepMTS` (multivariate time series forecasting): see https://github.com/Techtonique/nnetsauce/blob/master/nnetsauce/demo/thierrymoudiki_20240106_LazyDeepMTS.ipynb
- Spaghetti plots for `MTS` and `DeepMTS` (multivariate time series forecasting): see https://thierrymoudiki.github.io/blog/2024/01/15/python/quasirandomizednn/forecasting/DeepMTS
- Subsample continuous and discrete responses