class feature_engine.discretisation.DecisionTreeDiscretiser(variables=None, cv=3, scoring='neg_mean_squared_error', param_grid=None, regression=True, random_state=None)[source]#

The DecisionTreeDiscretiser() replaces numerical variables by discrete, i.e., finite variables, which values are the predictions of a decision tree.

The method is inspired by the following article from the winners of the KDD 2009 competition:

The DecisionTreeDiscretiser() trains a decision tree per variable. Then, it transforms the variables, with predictions of the decision tree.

The DecisionTreeDiscretiser() works only with numerical variables. A list of variables to transform can be indicated. Alternatively, the discretiser will automatically select all numerical variables.

More details in the User Guide.

variables: list, default=None

The list of numerical variables to transform. If None, the transformer will automatically find and select all numerical variables.

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.

scoring: str, default=’neg_mean_squared_error’

Desired metric to optimise the performance of the tree. Comes from sklearn.metrics. See the DecisionTreeRegressor or DecisionTreeClassifier model evaluation documentation for more options:

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 discretiser should train a regression or a classification decision tree.

random_stateint, 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.


Dictionary containing the fitted tree per variable.


Dictionary with the score of the best decision tree per variable.


The group of variables that will be transformed.


List with the names of features seen during fit.


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



Niculescu-Mizil, et al. “Winning the KDD Cup Orange Challenge with Ensemble Selection”. JMLR: Workshop and Conference Proceedings 7: 23-34. KDD 2009


>>> import pandas as pd
>>> import numpy as np
>>> from feature_engine.discretisation import DecisionTreeDiscretiser
>>> np.random.seed(42)
>>> X = pd.DataFrame(dict(x= np.random.randint(1,100, 100)))
>>> y_reg = pd.Series(np.random.randn(100))
>>> dtd = DecisionTreeDiscretiser(random_state=42)
>>>, y_reg)
>>> dtd.transform(X)["x"].value_counts()
-0.090091    90
0.479454    10
Name: x, dtype: int64

You can also apply this for classification problems adjusting the scoring metric.

>>> y_clf = pd.Series(np.random.randint(0,2,100))
>>> dtd = DecisionTreeDiscretiser(regression=False, scoring="f1", random_state=42)
>>>, y_clf)
>>> dtd.transform(X)["x"].value_counts()
0.480769    52
0.687500    48
Name: x, dtype: int64



Fit a decision tree per variable.


Fit to data, then transform it.


Get output feature names for transformation.


Get parameters for this estimator.


Set the parameters of this estimator.


Replace continuous variable values by the predictions of the decision tree.

fit(X, y)[source]#

Fit one decision tree per variable to discretize with cross-validation and grid-search for hyperparameters.

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

The training dataset. Can be the entire dataframe, not just the variables to be transformed.

y: pandas series.

Target variable. Required to train the decision tree.

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. In other words, returns the variable names of transformed dataframe.

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_.

feature_names_out: list

Transformed feature names.


List[Union[str, int]] ..


Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.


A MetadataRequest encapsulating routing information.


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.


Replaces original variable values with the predictions of the tree. The decision tree predictions are finite, aka, discrete.

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

The input samples.

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

The dataframe with transformed variables.


DataFrame ..