UnbiasedGBM

repository for Unbiased Gradient Boosting Decision Tree with Unbiased Feature Importance

model

UPD: 6.12

This part is our classification/regression model.

bash compile.sh
python3 example.py

UnbiasedBoost

• losstool: 'logloss' for classification or 'MSE' for regression
• n_est: number of estimators
• min_leaf: minimum number of instances at leaf node
• lr: learning rate
• n_leaf: number of leaves per tree.

.fit

• df: dataframe. categorical features should input as int, and numerical feature should input as float.
• label
• testset: tuple(metric, df_test, df_label) (see example.py)
• return_pred: whether to return prediction of testset

.predict

• df: dataframe.

.calc_self_imp

This method will return the importance in training stage, different from our post-hoc method.

UnbiasedGain

This part is a post-hoc method.

Support XGBoost LightGBM

UnbiasedGain.calc_gain(model, dataT, labelT, dataV, labelV, losstool)

Example Code

seed = 998244353
model = (lgb.LGBMRegressor if task=='regression' else lgb.LGBMClassifier)(random_state=seed, learning_rate=1, n_estimators=5)
model.fit(X_train, y_train.values)
pred = model.predict(X_test) if task=='regression' else model.predict_proba(X_test)[:,1]
print(model.feature_importances_)
losstool = UnbiasedGain.MSE_tool() if task=='regression' else UnbiasedGain.logloss_tool()
UnbiasedGain.calc_gain(model, X_train, y_train, X_test, y_test, losstool)