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 {feature:capping value}
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 (maximum values).
- min_capping_dict: dictionary, default=None
Dictionary containing user specified capping values for the eft tail of the distribution of each variable (minimum values).
- missing_valuesstring, default=’raise’
Indicates if missing values should be ignored or raised. If
missing_values='raise'the transformer will return an error if the training or the datasets to transform contain missing values.
- Attributes
- right_tail_caps_:
Dictionary with the maximum values at which variables will be capped.
- left_tail_caps_:
Dictionary with the minimum values at which variables will be capped.
- variables_:
The group of variables that will be transformed.
- n_features_in_:
The number of features in the train set used in fit.
Methods
fit:
This transformer does not learn any parameter.
transform:
Cap the variables.
fit_transform:
Fit to the data. Then transform it.
- 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_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.