DecisionTreeEncoder

class feature_engine.encoding.DecisionTreeEncoder(encoding_method='arbitrary', cv=3, scoring='neg_mean_squared_error', param_grid=None, regression=True, random_state=None, variables=None, ignore_format=False, precision=None, unseen='ignore', fill_value=None)

The DecisionTreeEncoder() encodes categorical variables with the predictions of a decision tree.

The encoder fits a single feature decision tree to predict the target, and with that, it creates mappings from category to prediction value. Then, it uses these mappings to replace the categories of the feature. The encoder trains a decision tree per feature to encode.

The DecisionTreeEncoder() will encode only categorical variables by default (type ‘object’ or ‘categorical’). You can pass a list of variables to encode or the encoder will find and encode all categorical variables.

With ignore_format=True you have the option to encode numerical variables as well. In this case, you can either enter the list of variables to encode, or the transformer will automatically select all variables.

More details in the User Guide.

Parameters

encoding_method: str, default=’arbitrary’

The method used to encode the categories to numerical values before fitting the decision tree.

‘ordered’: the categories are numbered in ascending order according to the target mean value per category.

‘arbitrary’ : categories are numbered arbitrarily.

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

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

• None, to use cross_validate’s default 5-fold cross validation

• int, to specify the number of folds in a (Stratified)KFold,

• CV splitter

  • (https://scikit-learn.org/stable/glossary.html#term-CV-splitter)

• An iterable yielding (train, test) splits as arrays of indices.

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.

scoring: str, default=’neg_mean_squared_error’

Desired metric to optimise the performance for the decision tree. Comes from sklearn.metrics. See the DecisionTreeRegressor or DecisionTreeClassifier model evaluation documentation for more options: https://scikit-learn.org/stable/modules/model_evaluation.html

param_grid: dictionary, default=None

The hyperparameters for the decision tree to test with a grid search. The param_grid can contain any of the permitted hyperparameters for Scikit-learn’s DecisionTreeRegressor() or DecisionTreeClassifier(). If None, then param_grid will optimise the ‘max_depth’ over [1, 2, 3, 4].

regression: boolean, default=True

Indicates whether the encoder should train a regression or a classification decision tree.

random_state: int, default=None

The random_state to initialise the training of the decision tree. It is one of the parameters of the Scikit-learn’s DecisionTreeRegressor() or DecisionTreeClassifier(). For reproducibility it is recommended to set the random_state to an integer.

variables: list, default=None

The list of categorical variables that will be encoded. If None, the encoder will find and transform all variables of type object or categorical by default. You can also make the transformer accept numerical variables, see the parameter ignore_format.

ignore_format: bool, default=False

This transformer operates only on variables of type object or categorical. To override this behaviour and allow the transformer to transform numerical variables as well, set to True.

If ignore_format is False, the encoder will automatically select variables of type object or categorical, or check that the variables entered by the user are of type object or categorical. If True, the encoder will select all variables or accept all variables entered by the user, including those cast as numeric.

In short, set to True when you want to encode numerical variables.

precision: int, default=None

The precision at which to store and display the category mappings. In other words, the number of decimals after the comma for the tree predictions.

unseen: string, default=’ignore’

Indicates what to do when categories not present in the train set are encountered during transform. If 'raise', then unseen categories will raise an error. If 'ignore', then unseen categories will be encoded as NaN and a warning will be raised instead. If 'encode' unseen categories will be encoded as fill_value.

fill_value: float, default=None

The value used to encode unseen categories. Only used when unseen='encode'.

Attributes

encoder_dict_:

Dictionary with the prediction per category, per variable.

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.

See also

sklearn.ensemble.DecisionTreeRegressor

sklearn.ensemble.DecisionTreeClassifier

feature_engine.discretisation.DecisionTreeDiscretiser

feature_engine.encoding.RareLabelEncoder

feature_engine.encoding.OrdinalEncoder

Notes

The authors designed this method originally to work with numerical variables. We can replace numerical variables by the predictions of a decision tree utilising the DecisionTreeDiscretiser(). Here, we extend this functionality to work also with categorical variables.

References

1

Niculescu-Mizil, et al. “Winning the KDD Cup Orange Challenge with Ensemble Selection”. JMLR: Workshop and Conference Proceedings 7: 23-34. KDD 2009 http://proceedings.mlr.press/v7/niculescu09/niculescu09.pdf

Examples

>>> import pandas as pd
>>> from feature_engine.encoding import DecisionTreeEncoder
>>> X = pd.DataFrame(dict(x1 = [1,2,3,4,5], x2 = ["b", "b", "b", "a", "a"]))
>>> y = pd.Series([2.2,4, 1.5, 3.2, 1.1])
>>> dte = DecisionTreeEncoder(cv=2)
>>> dte.fit(X, y)
>>> dte.transform(X)
   x1        x2
0   1  2.566667
1   2  2.566667
2   3  2.566667
3   4  2.150000
4   5  2.150000

You can also use it for classification by using regression=False.

>>> y = pd.Series([0,1,1,1,0])
>>> dte = DecisionTreeEncoder(regression=False, cv=2)
>>> dte.fit(X, y)
>>> dte.transform(X)
   x1        x2
0   1  0.666667
1   2  0.666667
2   3  0.666667
3   4  0.500000
4   5  0.500000

Methods

fit:	Fit a decision tree per variable.
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.
inverse_transform:	Convert the data back to the original representation.
transform:	Replace categorical variable by the predictions of the decision tree.

fit(X, y)

Fit a decision tree per variable.

Parameters

Xpandas dataframe of shape = [n_samples, n_features]

The training input samples. Can be the entire dataframe, not just the categorical variables.

ypandas series.

The target variable. Required to train the decision tree and for ordered ordinal encoding.

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

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

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. Pass only if the estimator accepts additional params in its fit method.

Returns

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_feature_names_out(input_features=None)

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, then feature_names_in_ is used as feature names in.

• If an array or list, then input_features must match feature_names_in_.

Returns

feature_names_out: list

Transformed feature names.

rtype

List[Union[str, int]] ..

get_metadata_routing()

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns

routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)

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.

inverse_transform(X)

Convert the encoded variable back to the original values.

Parameters

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

The transformed dataframe.

Returns

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

The un-transformed dataframe, with the categorical variables containing the original values.

rtype

DataFrame ..

set_params(**params)

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)

Replace categorical variables by the predictions of the decision tree.

Parameters

Xpandas dataframe of shape = [n_samples, n_features]

The input samples.

Returns

X_newpandas dataframe of shape = [n_samples, n_features].

Dataframe with variables encoded with decision tree predictions.

rtype

DataFrame ..