v0.5.8: lightgbm treeviz, LinearSVC support, logodds fixes

Version 0.5.8:

Bug Fixes

• Fix issue #198: handle LightGBM models with string categorical features in SHAP/dashboard paths by normalizing tree-SHAP evaluation input for categorical columns.
• Prevent crashes in LightGBM what-if/SHAP flows caused by object/string categorical values during SHAP value computation.
• Fix CatBoost PDP/dashboard callback crashes when categorical values in X_row are missing (NaN) by preserving dataframe categorical handling and sanitizing CatBoost categorical prediction inputs.
• Fix issue #146: ExplainerHub.to_yaml(..., integrate_dashboard_yamls=True) now honors pickle_type instead of hardcoding .joblib, and correctly dumps explainer files when dump_explainers=True.
• Fix issue #294: align multiclass model_output='logodds' semantics across Prediction Box and Contributions Plot by using per-class raw margins for multiclass logodds displays.
• Fix multiclass PDP highlight predictions in logodds mode to use the same raw-margin scale as SHAP contributions.
• Fix XGBoost multiclass decision-path summary wording to display prediction (logodds) when explainer model_output='logodds'.
• Fix issue #256: add robust multiclass probability fallback for classifiers that expose decision_function but not predict_proba (e.g. LinearSVC), and use it consistently across kernel SHAP, prediction helpers, PDP, and permutation scorer paths.
• Prevent multiclass class-count mismatches when user-provided/broken predict_proba outputs do not match model class count by falling back to decision_function-based probabilities.
• Fix issue #118: add LightGBM decision-tree visualization support (dtreeviz) across explainer auto-detection, tree plotting, and decision-path rendering in dashboard tree tabs.
• Fix dtreeviz callback rendering on macOS by switching matplotlib to a non-interactive backend for off-main-thread tree rendering to prevent dashboard 500 errors.