DropConstantFeatures#
- class feature_engine.selection.DropConstantFeatures(variables=None, tol=1, missing_values='raise')[source]#
DropConstantFeatures() drops constant and quasi-constant variables from a dataframe. Constant variables show the same value in all the observations in the dataset. Quasi-constant variables show the same value in almost all the observations in the dataset.
This transformer works with numerical and categorical variables. The user can indicate a list of variables to examine. Alternatively, the transformer will evaluate all the variables in the dataset.
The transformer will first identify and store the constant and quasi-constant variables. Next, the transformer will drop these variables from a dataframe.
More details in the User Guide.
- Parameters
- variables: list, default=None
The list of variables to evaluate. If None, the transformer will evaluate all variables in the dataset.
- tol: float,int, default=1
Threshold to detect constant/quasi-constant features. Variables showing the same value in a percentage of observations greater than tol will be considered constant / quasi-constant and dropped. If tol=1, the transformer removes constant variables. Else, it will remove quasi-constant variables. For example, if tol=0.98, the transformer will remove variables that show the same value in 98% of the observations.
- missing_values: str, default=raises
Whether the missing values should be raised as error, ignored or included as an additional value of the variable. Takes values ‘raise’, ‘ignore’, ‘include’.
- Attributes
- features_to_drop_:
List with constant and quasi-constant features.
- variables_:
The variables that will be considered for the feature selection.
- n_features_in_:
The number of features in the train set used in fit.
See also
sklearn.feature_selection.VarianceThreshold
Notes
This transformer is a similar concept to the VarianceThreshold from Scikit-learn, but it evaluates number of unique values instead of variance.
Methods
fit:
Find constant and quasi-constant features.
transform:
Remove constant and quasi-constant features.
fit_transform:
Fit to the data. Then transform it.
- fit(X, y=None)[source]#
Find constant and quasi-constant features.
- Parameters
- X: pandas dataframe of shape = [n_samples, n_features]
The input dataframe.
- y: None
y is not needed for 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.
- transform(X)[source]#
Return dataframe with selected features.
- Parameters
- X: pandas dataframe of shape = [n_samples, n_features].
The input dataframe.
- Returns
- X_new: pandas dataframe of shape = [n_samples, n_selected_features]
Pandas dataframe with the selected features.
- :rtype:py:class:
~pandas.core.frame.DataFrame