ArbitraryOutlierCapper¶
- class feature_engine.outliers.ArbitraryOutlierCapper(max_capping_dict=None, min_capping_dict=None, missing_values='raise')[source]¶
The ArbitraryOutlierCapper() caps the maximum or minimum values of a variable at an arbitrary value indicated by the user.
You must provide the maximum or minimum values that will be used to cap each variable in a dictionary containing the features as keys and the capping values as values.
More details in the User Guide.
- Parameters
- max_capping_dict: dictionary, default=None
Dictionary containing the user specified capping values for the right tail of the distribution of each variable to cap (maximum values).
- min_capping_dict: dictionary, default=None
Dictionary containing user specified capping values for the eft tail of the distribution of each variable to cap (minimum values).
- missing_values: string, default=’raise’
Indicates if missing values should be ignored or raised. If ‘raise’ the transformer will return an error if the the datasets to
fitortransformcontain missing values. If ‘ignore’, missing data will be ignored when learning parameters or performing the transformation.
- Attributes
- right_tail_caps_:
Dictionary with the maximum values beyond which a value will be considered an outlier.
- left_tail_caps_:
Dictionary with the minimum values beyond which a value will be considered an outlier.
- variables_:
The group of variables that will be transformed.
- feature_names_in_:
List with the names of features seen during
fit.- n_features_in_:
The number of features in the train set used in fit.
Methods
fit:
This transformer does not learn parameters.
fit_transform:
Fit to data, then transform it.
get_feature_names_out:
Get output feature names for transformation.
get_params:
Get parameters for this estimator.
set_params:
Set the parameters of this estimator.
transform:
Cap the variables.
- fit(X, y=None)[source]¶
This transformer does not learn any parameter.
- Parameters
- X: pandas dataframe of shape = [n_samples, n_features]
The training input samples.
- y: pandas Series, default=None
y is not needed in this transformer. You can pass y or None.
- fit_transform(X, y=None, **fit_params)[source]¶
Fit to data, then transform it.
Fits transformer to
Xandywith optional parametersfit_paramsand returns a transformed version ofX.- Parameters
- Xarray-like of shape (n_samples, n_features)
Input samples.
- yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None
Target values (None for unsupervised transformations).
- **fit_paramsdict
Additional fit parameters.
- Returns
- X_newndarray array of shape (n_samples, n_features_new)
Transformed array.
- get_feature_names_out(input_features=None)[source]¶
Get output feature names for transformation.
- Parameters
- input_features: str, list, default=None
If
None, then the names of all the variables in the transformed dataset is returned. If list with feature names, the features in the list will be returned. This parameter exists mostly for compatibility with the Scikit-learn Pipeline.
- Returns
- get_params(deep=True)[source]¶
Get parameters for this estimator.
- Parameters
- deepbool, default=True
If True, will return the parameters for this estimator and contained subobjects that are estimators.
- Returns
- paramsdict
Parameter names mapped to their values.
- set_params(**params)[source]¶
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as
Pipeline). The latter have parameters of the form<component>__<parameter>so that it’s possible to update each component of a nested object.- Parameters
- **paramsdict
Estimator parameters.
- Returns
- selfestimator instance
Estimator instance.