Bluecast

Taking various steps to increase the performance (especially for regression tasks):
* Set `min_child_weight` and `col_sample_by_level` to zero spans (min and max are equal) to speed up finding optimal spots for more important parameters. However users can still adjust these ranges on demand
* Fix range of `subsample` param which exceeded 1
* Remove adjusted losses
* Add multiple mean strategies to blend predictions of sub models in `BlueCastCVRegression`
* Updated poetry env
* Add optional sampling during training
* Enabled `use_full_data_for_final_model` by default

Better training performance:
* Updated hyperparameter tuning spaces
* Removed random seed tuning

* Update poetry lock to cover security updates of dependencies
* Reduce number of dependency plots shown by default from 5 to 1
* add conformal prediction for classification and regression
* add `predict_proba` methods for `BlueCast` and `BlueCastCV` classes
* add conformal prediction methods for `BlueCast` and `BlueCastCV`classes
* add string label handling to conformal prediction such as `BlueCast` & `BlueCastCV` return a set of labels rather than numeric encodings
* update how FeatureTypeDetector overwrites casted columns
* add new unit tests
* fix the `predict_proba` from multiclass tasks where sub model values aren't returned
* update the gpu support check to stop throwing the deprecation warnings for newer Xboost versions
* add eCDF function
* refresh and reorganize documentation
* add optional creation of Shap values during inference so users can plot the waterfall chart for any id/row from the new data

Adding additional model explainability:
* add SHAP waterfall charts plot
* add SHAP dependence plots
* enhance documentation with examples

This PR contains API changes alongside convenience and performance features:
* BlueCast & BlueCastRegression do not require the target column name during class instantiation anymore.
* t-SNE & PCA plots will scale input data automatically if not disabled by the user
* add `eta` param in grid search fine tuning
* raise `max_depth` in Xgboost param default
* add category encoding orchestration: If cardinality of a categorical column is below/equal a certain threshold, onehot encoding will be assigned, otherwise target encoding

The focus of this release has been EDA and explainability,
* add unit test for BlueCastCV in multiclass scenarios
* add calculation and print of out-of-fold scores for `fit_eval` in CV classes
* add new EDA visual: pairplot counts
* add new EDA visual: pie chart
* add new EDA visual: cumulative principal component variance