class feature_engine.selection.SelectBySingleFeaturePerformance(estimator, scoring='roc_auc', cv=3, threshold=None, variables=None, confirm_variables=False)[source]#

SelectBySingleFeaturePerformance() selects features based on the performance of a machine learning model trained utilising a single feature. In other words, it trains a machine learning model for every single feature, then determines each model’s performance. If the performance of the model is greater than a user specified threshold, then the feature is retained, otherwise removed.

The models are trained on each individual features using cross-validation. The performance metric to evaluate and the machine learning model to train are specified by the user.

More details in the User Guide.

estimator: object

A Scikit-learn estimator for regression or classification.

variables: str or list, default=None

The list of variables to evaluate. If None, the transformer will evaluate all numerical features in the dataset.

scoring: str, default=’roc_auc’

Metric to evaluate the performance of the estimator. Comes from sklearn.metrics. See the model evaluation documentation for more options:

threshold: float, int, default = 0.01

The value that defines whether a feature will be selected. Note that for metrics like the roc-auc, r2, and the accuracy, the threshold will be a float between 0 and 1. For metrics like the mean squared error and the root mean squared error, the threshold can take any number. The threshold must be defined by the user. With bigger thresholds, fewer features will be selected.

cv: int, cross-validation generator or an iterable, default=3

Determines the cross-validation splitting strategy. Possible inputs for cv are:

For int/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used. These splitters are instantiated with shuffle=False so the splits will be the same across calls. For more details check Scikit-learn’s cross_validate’s documentation.

confirm_variables: bool, default=False

If set to True, variables that are not present in the input dataframe will be removed from the list of variables. Only used when passing a variable list to the parameter variables. See parameter variables for more details.


List with the features that will be removed.


Dictionary with the single feature model performance per feature.


The variables that will be considered for the feature selection procedure.


List with the names of features seen during fit.


The number of features in the train set used in fit.


Selection based on single feature performance was used in Credit Risk modelling as discussed in the following talk at PyData London 2017:


Galli S. “Machine Learning in Financial Risk Assessment”.



Find the important features.


Fit to data, then transform it.


Get output feature names for transformation.


Get parameters for this estimator.


Set the parameters of this estimator.


Reduce X to the selected features.

fit(X, y)[source]#

Select features.

X: pandas dataframe of shape = [n_samples, n_features]

The input dataframe

y: array-like of shape (n_samples)

Target variable. Required to train the estimator.

fit_transform(X, y=None, **fit_params)[source]#

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

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).


Additional fit parameters.

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.


Get output feature names for transformation.

input_features: None

This parameter exists only for compatibility with the Scikit-learn pipeline, but has no functionality. You can pass a list of feature names or None.

feature_names_out: list

The feature names.


Get parameters for this estimator.

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.


Parameter names mapped to their values.


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.


Estimator parameters.

selfestimator instance

Estimator instance.


Return dataframe with selected features.

X: pandas dataframe of shape = [n_samples, n_features].

The input dataframe.

X_new: pandas dataframe of shape = [n_samples, n_selected_features]

Pandas dataframe with the selected features.