How come you can get a permutation feature importance greater than 1?

user_1473517 0
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Take this simple code:

import lightgbm as lgb
from sklearn.inspection import permutation_importance
X_train, X_test, y_train, y_test = train_test_split(X, y)
lgbr = lgb.LGBMRegressor(n_estimators=500, learning_rate=0.15)
model_lgb = lgbr.fit(X_train, y_train)
r = permutation_importance(model_lgb, X_test, y_test, n_repeats=30, random_state=0)
for i in r.importances_mean.argsort()[::-1]:
    print(f"{i} " f"{r.importances_mean[i]:.3f}" f" +/- {r.importances_std[i]:.3f}")

When I run this on my dataset the top value is about 1.20.

But I thought that the permutation_importance mean for a feature was the amount that the score was changed on average by permuting the feature column so this can't be more than 1 can it?

What am I missing?

(I get the same issue if I replace lightgbm by xgboost so I don't think it is a feature of the particular regression method.)

scikit-learn python
1 Answers

But I thought that the permutation_importance mean for a feature was the amount that the score was changed on average by permuting the feature column[...]

Correct.

so this can't be more than 1 can it?

That depends on whether the score can "worsen" by more than 1. The default for the scoring parameter of permutation_importance is None, which uses the model's score function. For LGBMRegressor (and most regressors), that's the R2 score, which has a maximum of 1 but can take arbitrarily large negative values, so indeed the score can worsen by an arbitrarily large amount.

user_104958930
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