decision_tree_regressor#
Decision Tree Regressor.
Module Contents#
Classes Summary#
Decision Tree Regressor. |
Contents#
- class evalml.pipelines.components.estimators.regressors.decision_tree_regressor.DecisionTreeRegressor(criterion='mse', max_features='auto', max_depth=6, min_samples_split=2, min_weight_fraction_leaf=0.0, random_seed=0, **kwargs)[source]#
Decision Tree Regressor.
- Parameters
criterion ({"mse", "friedman_mse", "mae", "poisson"}) –
The function to measure the quality of a split. Supported criteria are:
”mse” for the mean squared error, which is equal to variance reduction as feature selection criterion and minimizes the L2 loss using the mean of each terminal node
”friedman_mse”, which uses mean squared error with Friedman”s improvement score for potential splits
”mae” for the mean absolute error, which minimizes the L1 loss using the median of each terminal node,
”poisson” which uses reduction in Poisson deviance to find splits.
max_features (int, float or {"auto", "sqrt", "log2"}) –
The number of features to consider when looking for the best split:
If int, then consider max_features features at each split.
If float, then max_features is a fraction and int(max_features * n_features) features are considered at each split.
If “auto”, then max_features=sqrt(n_features).
If “sqrt”, then max_features=sqrt(n_features).
If “log2”, then max_features=log2(n_features).
If None, then max_features = n_features.
The search for a split does not stop until at least one valid partition of the node samples is found, even if it requires to effectively inspect more than max_features features.
max_depth (int) – The maximum depth of the tree. Defaults to 6.
min_samples_split (int or float) –
The minimum number of samples required to split an internal node:
If int, then consider min_samples_split as the minimum number.
If float, then min_samples_split is a fraction and ceil(min_samples_split * n_samples) are the minimum number of samples for each split.
Defaults to 2.
min_weight_fraction_leaf (float) – The minimum weighted fraction of the sum total of weights (of all the input samples) required to be at a leaf node. Defaults to 0.0.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{ “criterion”: [“mse”, “friedman_mse”, “mae”], “max_features”: [“auto”, “sqrt”, “log2”], “max_depth”: Integer(4, 10),}
model_family
ModelFamily.DECISION_TREE
modifies_features
True
modifies_target
False
name
Decision Tree Regressor
supported_problem_types
[ ProblemTypes.REGRESSION, ProblemTypes.TIME_SERIES_REGRESSION,]
training_only
False
Methods
Constructs a new component with the same parameters and random state.
Returns the default parameters for this component.
Describe a component and its parameters.
Returns importance associated with each feature.
Fits estimator to data.
Loads component at file path.
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
Returns the parameters which were used to initialize the component.
Make predictions using selected features.
Make probability estimates for labels.
Saves component at file path.
- clone(self)#
Constructs a new component with the same parameters and random state.
- Returns
A new instance of this component with identical parameters and random state.
- default_parameters(cls)#
Returns the default parameters for this component.
Our convention is that Component.default_parameters == Component().parameters.
- Returns
Default parameters for this component.
- Return type
dict
- describe(self, print_name=False, return_dict=False)#
Describe a component and its parameters.
- Parameters
print_name (bool, optional) – whether to print name of component
return_dict (bool, optional) – whether to return description as dictionary in the format {“name”: name, “parameters”: parameters}
- Returns
Returns dictionary if return_dict is True, else None.
- Return type
None or dict
- property feature_importance(self)#
Returns importance associated with each feature.
- Returns
Importance associated with each feature.
- Return type
np.ndarray
- Raises
MethodPropertyNotFoundError – If estimator does not have a feature_importance method or a component_obj that implements feature_importance.
- fit(self, X, y=None)#
Fits estimator to data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series, optional) – The target training data of length [n_samples].
- Returns
self
- static load(file_path)#
Loads component at file path.
- Parameters
file_path (str) – Location to load file.
- Returns
ComponentBase object
- needs_fitting(self)#
Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
This can be overridden to False for components that do not need to be fit or whose fit methods do nothing.
- Returns
True.
- property parameters(self)#
Returns the parameters which were used to initialize the component.
- predict(self, X)#
Make predictions using selected features.
- Parameters
X (pd.DataFrame) – Data of shape [n_samples, n_features].
- Returns
Predicted values.
- Return type
pd.Series
- Raises
MethodPropertyNotFoundError – If estimator does not have a predict method or a component_obj that implements predict.
- predict_proba(self, X)#
Make probability estimates for labels.
- Parameters
X (pd.DataFrame) – Features.
- Returns
Probability estimates.
- Return type
pd.Series
- Raises
MethodPropertyNotFoundError – If estimator does not have a predict_proba method or a component_obj that implements predict_proba.
- save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL)#
Saves component at file path.
- Parameters
file_path (str) – Location to save file.
pickle_protocol (int) – The pickle data stream format.