text: Title: [DOCS] Example usage in docstring
  Body: As [discussed in #586](https://github.com/koaning/scikit-lego/pull/586#discussion_r1385475426), although most of the library features are documented in the user guide, the best way to showcase how to access and use each class/function would be a minimal example usage in the docstrings. And this is currently covered only for a subset of the library features.
  
  Here the list of all the remaining classes that would benefit from it:
  
  - [x] `preprocessing.outlier_remover.OutlierRemover` (solved in #639 )
  - [x] `meta.outlier_classifier.OutlierClassifier` (solved in #646)
  - [x] `preprocessing.dictmapper.DictMapper` (solved in #646)
  - [x] `preprocessing.pandastransformers.PandasTypeSelector` (solved in #648)
  - [x] `preprocessing.projections.InformationFilter` (solved in #648)
  - [x] `preprocessing.repeatingbasis.RepeatingBasisFunction` (solved in #648)
  - [x] `preprocessing.formulaictransformer.FormulaicTransformer` (solved in #648)
  - [x] `preprocessing.identitytransformer.IdentityTransformer` (solved in #648)
  - [x] #650
  - [x] `linear_model.ProbWeightRegression` (solved in #691)
  - [x] `linear_model.DeadZoneRegressor` (solved in #691)
  - [x] `linear_model.DemographicParityClassifier` (solved in #691)
  - [x] `linear_model.EqualOpportunityClassifier` (solved in #691)
  - [x] #652
  - [x] #653
  - [x] `mixture.bayesian_gmm_classifier.BayesianGMMClassifier`
  - [x] `mixture.bayesian_gmm_detector.BayesianGMMOutlierDetector`
  - [x] `mixture.gmm_classifier.GMMClassifier`
  - [x] `mixture.gmm_detector.GMMOutlierDetector`
  - [ ] `model_selection.TimeGapSplit`
  - [ ] `model_selection.GroupTimeSeriesSplit`
  - [ ] `model_selection.KlusterFoldValidation`
  - [ ] `naive_bayes.GaussianMixtureNB`
  - [ ] `naive_bayes.BayesianGaussianMixtureNB`
  - [ ] `neighbors.BayesianKernelDensityClassifier`
  - [ ] `meta.confusion_balancer.ConfusionBalancer`
  - [ ] `meta.estimator_transformer.EstimatorTransformer`
  - [ ] `meta.grouped_predictor.GroupedPredictor`
  - [ ] `meta.grouped_transformer.GroupedTransformer`
  - [ ] `meta.regression_outlier_detector.RegressionOutlierDetector`
  - [ ] `meta.subjective_classifier.SubjectiveClassifier`
  - [ ] `meta.thresholder.Thresholder`
  - [ ] `preprocessing.intervalencoder.IntervalEncoder`
  
  As an instance of such minimal example you can refer to [`QuantileRegression` docstring section](https://github.com/koaning/scikit-lego/blob/main/sklego/linear_model.py#L1140), which renders as in its [API section](https://koaning.github.io/scikit-lego/api/linear-model/#sklego.linear_model.QuantileRegression).
  
  If possible try to add **one** unique example covering the relevant features and methods in the top level docstring of the class.