Calculates permutation importance for features.
pipeline (PipelineBase or subclass) – Fitted pipeline
X (ww.DataTable, pd.DataFrame) – The input data used to score and compute permutation importance
y (ww.DataColumn, 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.
random_state (int, np.random.RandomState) – The random seed/state. Defaults to 0.
Mean feature importance scores over 5 shuffles.