SelectByInformationValue#
- class feature_engine.selection.SelectByInformationValue(variables=None, bins=5, strategy='equal_width', threshold=0.2, confirm_variables=False)[source]#
SelectByInformationValue() selects features based on their information value (IV). The IV is calculated as:
\[IV = ∑ (fraction of positive cases - fraction of negative cases) * WoE\]where:
- the fraction of positive cases is the proportion of observations of class 1,
from the total class 1 observations.
- the fraction of negative cases is the proportion of observations of class 0,
from the total class 0 observations.
WoE is the weight of the evidence.
SelectByInformationValue() is only suitable to select features for binary classification.
SelectByInformationValue() can determine the IV for numerical and categorical variables. For numerical variables, it first sorts the variables into intervals, and then determines the IV.
You can pass a list of variables to examine. Alternatively, the transformer will examine all variables.
The IV allows you to assess each variable’s independent contribution to the target variable. The transformer selects those variables whose IV is higher than the threshold.
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 (except datetime).
- bins: int, default = 5
If the dataset contains numerical variables, the number of bins into which the values will be sorted.
- strategy: str, default = ‘equal_width’
Whether the bins should be of equal width (‘equal_width’) or equal frequency (‘equal_frequency’).
- threshold: float, int, default = 0.2.
The threshold to drop a feature. If the IV for a feature is < threshold, the feature will be dropped.
- 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.
- Attributes
- variables_:
The group of variables that will be transformed.
- information_values_:
A dictionary with the information values for each feature.
- features_to_drop_:
List with the features that will be removed.
- 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.
See also
References
- 1
Weight of evidence and information value explained https://www.listendata.com/2015/03/weight-of-evidence-woe-and-information.html
- 2
WoE and IV for continuous variables https://www.listendata.com/2019/08/WOE-IV-Continuous-Dependent.html
Examples
>>> import pandas as pd >>> from feature_engine.selection import SelectByInformationValue >>> X = pd.DataFrame(dict(x1 = [1,1,1,1,1,1], >>> x2 = [3,2,2,3,3,2], >>> x3 = ["a","b","c","a","c","b"])) >>> y = pd.Series([1,1,1,0,0,0]) >>> iv = SelectByInformationValue() >>> iv.fit_transform(X, y) x2 0 3 1 2 2 2 3 3 4 3 5 2
Methods
fit:
Find features with high information value.
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.
get_support:
Get a mask, or integer index, of the features selected.
transform:
Remove features with low information value.
- fit(X, y)[source]#
Learn the information value. Find features with IV above the threshold.
- Parameters
- X: pandas dataframe of shape = [n_samples, n_features]
The training input samples.
- y: pandas series of shape = [n_samples, ]
Target, must be binary.
- 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. In other words, returns the variable names of transformed dataframe.
- Parameters
- input_featuresarray or list, default=None
This parameter exits only for compatibility with the Scikit-learn pipeline.
If
None, thenfeature_names_in_is used as feature names in.If an array or list, then
input_featuresmust matchfeature_names_in_.
- Returns
- feature_names_out: list
Transformed feature names.
- 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.
- get_support(indices=False)[source]#
Get a mask, or integer index, of the features selected.
- Parameters
- indicesbool, default=False
If True, the return value will be an array of integers, rather than a boolean mask.
- Returns
- supportarray
An index that selects the retained features from a feature vector. If
indicesis False, this is a boolean array of shape [# input features], in which an element is True if its corresponding feature is selected for retention. Ifindicesis True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.
- 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.