Supported models#

TreeIG currently supports tree models with finite numeric feature inputs.

Regression#

  • sklearn.tree.DecisionTreeRegressor

  • sklearn.ensemble.RandomForestRegressor

  • sklearn.ensemble.ExtraTreesRegressor

  • sklearn.ensemble.GradientBoostingRegressor

  • xgboost.XGBRegressor

  • xgboost.Booster

  • lightgbm.LGBMRegressor

  • lightgbm.Booster

Classification (raw margins/logits only)#

  • sklearn.ensemble.GradientBoostingClassifier

  • xgboost.XGBClassifier

  • lightgbm.LGBMClassifier

For classification models, exact structural TreeIG attributes raw margins or logits. Its current exact engine does not transform ensemble probabilities.

TreeIG computes exact path decompositions directly from the fitted tree structure. Since tree representations differ substantially across implementations, each model family requires customized parsing and routing logic.

Exact support not currently available#

The exact TreeIG parser does not currently support:

  • CatBoost;

  • categorical splits;

  • missing-value routing (use feature augmentation for missingness);

  • structurally exact transformed-probability attribution;

  • probability-averaging or vote-share classifiers such as DecisionTreeClassifier, RandomForestClassifier, and ExtraTreesClassifier (because they produce probabilities, not scores).

Many of these can still be attributed with the model-agnostic TreeIGNumeric, described below.