TreeRegressor#

class sklearn_nominal.TreeRegressor(criterion='std', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_error_decrease=1e-16, attribute_penalization_importance=1, nominal_split='multi', backend='pandas')[source]#

A decision tree regressor for nominal and numeric attributes.

This estimator mimics scikit-learn’s DecisionTreeRegressor but provides native support for nominal attributes without requiring pre-encoding. It builds a regression tree using a recursive partitioning approach.

Args:

criterion (str): The function to measure the quality of a split.: Supported criteria is “std” for standard deviation. Defaults to “std”.
splitter (str): The strategy used to choose the split at each node.: Supported strategies are “best” to choose the best split. Defaults to “best”.
max_depth (int, optional): The maximum depth of the tree. If None, then: nodes are expanded until all leaves are pure or until all leaves contain less than min_samples_split samples. Defaults to None.
min_samples_split (int): The minimum number of samples required to split: an internal node. Defaults to 2.
min_samples_leaf (int): The minimum number of samples required to be at: a leaf node. A split point at any depth will only be considered if it leaves at least min_samples_leaf training samples in each of the left and right branches. Defaults to 1.
min_error_decrease (float): A node will be split if this split induces: a decrease of the error greater than or equal to this value. Defaults to 1e-16.
nominal_split (str, optional): The strategy used to split nominal attributes.: See BaseTree. Defaults to “multi”.

backend (str): The backend used for data processing. Defaults to “pandas”.

Attributes:

n_features_in_ (int): Number of features seen during fit. feature_names_in_ (ndarray): Names of features seen during fit.

Defined only when X has feature names that are all strings.

n_outputs_ (int): The number of outputs when fit is performed. tree_ (sklearn_nominal.tree.tree.Tree): The underlying Tree object.

See Also:

BaseTree: Base class for tree-based estimators. TreeClassifier: A decision tree classifier.

Examples:

>>> from sklearn_nominal import TreeRegressor, read_golf_regression_dataset
>>> x, y = read_golf_regression_dataset(url)
>>> model = TreeRegressor(criterion="std", max_depth=4)
>>> model.fit(x, y)
>>> y_pred = model.predict(x)

Methods

`build_attribute_penalizer`()	Determines the penalization strategy for multi-valued attributes.
`build_error`(criterion)	Builds the regression error function for the given criterion.
`build_prune_criteria`(d)	Translates tree constraints into internal pruning criteria.
`build_splitter`(e, p)	Constructs specialized split scorers for different column types.
`check_is_fitted`()	Checks if the model has been fitted.
`complexity`()	Returns the complexity of the fitted model.
`display`([class_names, title])	Displays the tree using the default system viewer or notebook output.
`export_dot`([class_names, title])	Exports the tree as a Graphviz dot string.
`export_dot_file`(filepath[, class_names, title])	Exports the tree as a Graphviz dot file.
`export_image`(filepath[, class_names, title])	Exports the tree as an image file.
`fit`(x, y)	Fit the decision tree regressor according to the given training data.
`get_dtypes`(x)	Extracts and maps data types from the input.
`get_feature_names`()	Returns the names of the features seen during fit.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`make_model`(d)	Creates the Tree trainer for the model.
`predict`(x)	Predict regression value for X.
`pretty_print`([class_names])	Returns a string representation of the fitted model.
`score`(X, y[, sample_weight])	Return the coefficient of determination of the prediction.
`set_dtypes`(x)	Sets and persists the data types based on the input.
`set_fit_request`(*[, x])	Request metadata passed to the `fit` method.
`set_model`(model)	Sets the underlying backend model and marks it as fitted.
`set_params`(**params)	Set the parameters of this estimator.
`set_predict_request`(*[, x])	Request metadata passed to the `predict` method.
`set_score_request`(*[, sample_weight])	Request metadata passed to the `score` method.
`set_sklearn_tags`(tags)	Sets scikit-learn tags for the supervised nominal model.
`validate_data_fit_regression`(x, y)	Validates and prepares data for regression fitting.
`validate_data_predict`(x)	Validates and prepares input data for prediction.

build_attribute_penalizer()#

Determines the penalization strategy for multi-valued attributes.

This is used to implement “gain_ratio”, which penalizes nominal attributes with many levels to prevent overfitting.

Returns:

sklearn_nominal.shared.ColumnPenalization: The penalization strategy (e.g., GainRatioPenalization or NoPenalization).

build_error(criterion)#

Builds the regression error function for the given criterion.

Parameters:

criterionstr: The error criterion to use (e.g., “std” for standard deviation).

Returns:

TargetError: An instance of the requested error function.

Raises:

ValueError: If the criterion is not recognized.

build_prune_criteria(d)#

Translates tree constraints into internal pruning criteria.

This method converts user-facing parameters (which can be counts or fractions) into the absolute integer values required by the tree builders.

Parameters:

dDataset: The dataset used to calculate relative sample counts.

Returns:

PruneCriteria: The consolidated criteria used for pruning.

Return type:

PruneCriteria

build_splitter(e, p)#

Constructs specialized split scorers for different column types.

This method maps the general splitter and criterion parameters into concrete ColumnError implementations for both Numeric and Nominal features.

Parameters:

esklearn_nominal.shared.TargetError: The target error function (e.g., Gini, Entropy, or MSE).
psklearn_nominal.shared.ColumnPenalization: The column-level penalization strategy.

Returns:

dict: A dictionary mapping ColumnType to its corresponding ColumnError scorer.

Raises:

ValueError: If the splitter value is neither “best” nor an integer, or if nominal_split is invalid.

check_is_fitted()#

Checks if the model has been fitted.

Raises:

NotFittedError: If the is_fitted_ attribute is not set or is False.

complexity()#

Returns the complexity of the fitted model.

The definition of complexity is backend and model dependent. For trees, it typically represents the number of nodes.

Returns:

int or float: The complexity metric of the model.

Raises:

NotFittedError: If the model has not been fitted yet.

display(class_names=None, title='')#

Displays the tree using the default system viewer or notebook output.

Parameters:

class_nameslist of str, optional: The names of the classes for display.
titlestr, default=””: The title for the graph.

Returns:

Any: The image object for display in interactive environments.

export_dot(class_names=None, title='')#

Exports the tree as a Graphviz dot string.

Parameters:

class_nameslist of str, optional: The names of the classes for display.
titlestr, default=””: The title for the graph.

Returns:

str: The tree in Graphviz dot format.

export_dot_file(filepath, class_names=None, title='')#

Exports the tree as a Graphviz dot file.

Parameters:

filepathstr: The path to the file to save.
class_nameslist of str, optional: The names of the classes for display.
titlestr, default=””: The title for the graph.

export_image(filepath, class_names=None, title='')#

Exports the tree as an image file.

This requires Graphviz to be installed on the system.

Parameters:

filepathstr: The path to the image file (e.g., “tree.png”).
class_nameslist of str, optional: The names of the classes for display.
titlestr, default=””: The title for the graph.

fit(x, y)[source]#

Fit the decision tree regressor according to the given training data.

This algorithm builds a regression tree using recursive partitioning. At each node, it selects the feature and the split (numeric or nominal) that minimizes the regression error (e.g., standard deviation). For nominal attributes, it creates a multi- way split corresponding to the attribute’s categories.

Args:: x (pd.DataFrame or np.ndarray): The training input samples. y (np.ndarray): The target values (real numbers).
Returns:: self: Returns the instance itself.

get_dtypes(x)#

Extracts and maps data types from the input.

This method identifies the data types of the input features to ensure they are correctly handled by the backend.

Parameters:

x{array-like, sparse matrix} of shape (n_samples, n_features): The input data.

Returns:

dict or None: A dictionary mapping column names to data types if x is a DataFrame, otherwise None.

get_feature_names()#

Returns the names of the features seen during fit.

Returns:

ndarray of str or None: The feature names, or None if they were not available during fit (e.g., if input was a numpy array).

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.

make_model(d)[source]#

Creates the Tree trainer for the model.

Args:: d (Dataset): The dataset to train on.
Returns:: BaseTreeTrainer: The tree trainer instance.

predict(x)[source]#

Predict regression value for X.

The predicted regression value for each input sample is obtained by traversing the decision tree from the root to a leaf node according to the sample’s feature values. The prediction is the mean of target values in that leaf node.

Args:: x (pd.DataFrame or np.ndarray): The input samples.
Returns:: np.ndarray: Predicted target values for X.

pretty_print(class_names=None)#

Returns a string representation of the fitted model.

Delegates the visualization logic to the underlying backend model.

Parameters:

class_nameslist of str, optional: Names of the classes to use in the output. If None, default identifiers are used.

Returns:

str: A human-readable representation of the model.

Raises:

NotFittedError: If the model has not been fitted yet.

score(X, y, sample_weight=None)#

Return the coefficient of determination of the prediction.

The coefficient of determination $R^2$ is defined as $(1 - \frac{u}{v})$, where $u$ is the residual sum of squares ((y_true - y_pred)** 2).sum() and $v$ is the total sum of squares ((y_true - y_true.mean()) ** 2).sum(). The best possible score is 1.0 and it can be negative (because the model can be arbitrarily worse). A constant model that always predicts the expected value of y, disregarding the input features, would get a $R^2$ score of 0.0.

Parameters:

Xarray-like of shape (n_samples, n_features): Test samples. For some estimators this may be a precomputed kernel matrix or a list of generic objects instead with shape (n_samples, n_samples_fitted), where n_samples_fitted is the number of samples used in the fitting for the estimator.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): True values for X.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.

Returns:

scorefloat: $R^2$ of self.predict(X) w.r.t. y.

Notes

The $R^2$ score used when calling score on a regressor uses multioutput='uniform_average' from version 0.23 to keep consistent with default value of r2_score(). This influences the score method of all the multioutput regressors (except for MultiOutputRegressor).

set_dtypes(x)#

Sets and persists the data types based on the input.

This is called during fit to ensure that subsequent calls to predict can cast the input data to the same types, preserving nominal/numeric distinctions.

Parameters:

x{pd.DataFrame, np.ndarray, sparse matrix}: The input data to extract types from.

Raises:

ValueError: If the input type is not supported or if the input is not 2D.

set_fit_request(*, x: bool | None | str = '$UNCHANGED$') → TreeRegressor#

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to fit.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

xstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for x parameter in fit.

Returns:

selfobject: The updated object.

set_model(model)#

Sets the underlying backend model and marks it as fitted.

Parameters:

modelsklearn_nominal.backend.core.Model: The trained model instance from the backend.

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.

set_predict_request(*, x: bool | None | str = '$UNCHANGED$') → TreeRegressor#

Request metadata passed to the predict method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to predict if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to predict.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

xstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for x parameter in predict.

Returns:

selfobject: The updated object.

set_score_request(*, sample_weight: bool | None | str = '$UNCHANGED$') → TreeRegressor#

Request metadata passed to the score method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see User Guide on how the routing mechanism works.

The options for each parameter are:

True: metadata is requested, and passed to score if provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it to score.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters:

sample_weightstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED: Metadata routing for sample_weight parameter in score.

Returns:

selfobject: The updated object.

set_sklearn_tags(tags)#

Sets scikit-learn tags for the supervised nominal model.

Parameters:

tagsTags: The scikit-learn tags object to be modified.

validate_data_fit_regression(x, y)#

Validates and prepares data for regression fitting.

This method ensures x and y are compatible, extracts data types, and packages them into a backend Dataset. It also ensures the target y is at least 2D for backend consistency.

Parameters:

xarray-like of shape (n_samples, n_features): The input features.
yarray-like of shape (n_samples,) or (n_samples, n_outputs): The target values.

Returns:

Dataset: The backend-specific dataset object.

Return type:

Dataset

validate_data_predict(x)#

Validates and prepares input data for prediction.

This method ensures the input features match the structure seen during training, handles feature name alignment, and restores data types.

Parameters:

xarray-like of shape (n_samples, n_features): The input data to validate.

Returns:

pd.DataFrame: The validated data as a pandas DataFrame, with dtypes restored to match those observed during training.

Raises:

NotFittedError: If the model has not been fitted yet.
ValueError: If the input contains no samples or has inconsistent features.

TreeRegressor#

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