Fairness aware AutoML on regression task¶
Please run the example script to train AutoML on Housing dataset with
large_B as sensitive feature.
Code from example script:
import numpy as np import pandas as pd from supervised.automl import AutoML df = pd.read_csv("./tests/data/boston_housing.csv") x_cols = [c for c in df.columns if c != "MEDV"] df["large_B"] = (df["B"] > 380) * 1 df["large_B"] = df["large_B"].astype(str) print(df["large_B"].dtype.name) sensitive_features = df["large_B"] X = df[x_cols] y = df["MEDV"] automl = AutoML( algorithms=["Xgboost", "LightGBM"], train_ensemble=True, fairness_threshold=0.9, ) automl.fit(X, y, sensitive_features=sensitive_features) df["predictions"] = automl.predict(X)
The example report with fairness metrics reported for each model generated automatically by AutoML: