data_describe.core.importance¶
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Variable importance chart. |
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data_describe.core.importance.
importance
(data, target: str, preprocess_func=None, estimator=None, return_values=False, truncate=True, compute_backend=None, viz_backend=None, **kwargs)¶ Variable importance chart.
This feature utilizes fits a simple model to the dataset to generate an estimate of feature importance (predictive power). Note that these results are dependent on the accuracy of the fitted model and should refined during modeling.
- Parameters
data – A Pandas data frame
target – Name of the response column, as a string
preprocess_func – A custom preprocessing function that takes a Pandas dataframe and the target/response column as a string. Returns X and y as tuple.
estimator – A custom sklearn estimator. Default is Random Forest Classifier
return_values – If True, only the importance values as a numpy array
truncate – If True, negative importance values will be truncated (set to zero)
compute_backend – The compute backend
viz_backend – The visualization backend
**kwargs – Other arguments to be passed to the preprocess function
- Returns
Matplotlib figure