v0.2.3.visualize trees inside xgboost

Version 0.2.3

Breaking Changes

• RandomForestClassifierExplainer and RandomForestRegressionExplainer will be
  deprecated: can now simply use ClassifierExplainer or RegressionExplainer and the
  mixin class will automatically be loaded.

New Features

• Now also support for visualizing individual trees for XGBoost models!
  (XGBClassifier and XGBRegressor). The XGBExplainer mixin class will be
  automatically loaded and make decisiontree_df(), decision_path() and plot_trees()
  methods available, the dashboard Decision Trees tab and components now also work for
  XGBoost models.
• new parameter n_jobs for calculations that can be parallelized (e.g. permutation importances)
• contrib_df, plot_shap_contributions: can order by global shap feature
  importance with sort='importance' (as well as 'abs', 'high-to-low'
  'low-to-high')
• added actual outcome to plot_trees (for both RandomForest and XGB)

Improvements

• optimized code for calculating permutation importance, adding possibility to calculate in parallel
• shap dependence component: if no color col selected, output standard blue dots instead of ignoring update

Other Changes

• added selenium browser based integration tests for dashboards (also working with github actions)
• added tests for multiclass classsification, DecisionTree and ExtraTrees models
• added tests for XGBExplainers
• added proper docstrings to explainer_methods.py