evalml.model_understanding.calculate_permutation_importance¶
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evalml.model_understanding.calculate_permutation_importance(pipeline, X, y, objective, n_repeats=5, n_jobs=None, random_seed=0)[source]¶ Calculates permutation importance for features.
- Parameters
pipeline (PipelineBase or subclass) – Fitted pipeline.
X (pd.DataFrame) – The input data used to score and compute permutation importance.
y (pd.Series) – The target data.
objective (str, ObjectiveBase) – Objective to score on.
n_repeats (int) – Number of times to permute a feature. Defaults to 5.
n_jobs (int or None) – Non-negative integer describing level of parallelism used for pipelines. None and 1 are equivalent. If set to -1, all CPUs are used. For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Defaults to None.
random_seed (int) – Seed for the random number generator. Defaults to 0.
- Returns
Mean feature importance scores over a number of shuffles.
- Return type
pd.DataFrame