baseline_regressor#
Baseline regressor that uses a simple strategy to make predictions. This is useful as a simple baseline regressor to compare with other regressors.
Module Contents#
Classes Summary#
Baseline regressor that uses a simple strategy to make predictions. This is useful as a simple baseline regressor to compare with other regressors. |
Contents#
- class evalml.pipelines.components.estimators.regressors.baseline_regressor.BaselineRegressor(strategy='mean', random_seed=0, **kwargs)[source]#
Baseline regressor that uses a simple strategy to make predictions. This is useful as a simple baseline regressor to compare with other regressors.
- Parameters
strategy (str) – Method used to predict. Valid options are “mean”, “median”. Defaults to “mean”.
random_seed (int) – Seed for the random number generator. Defaults to 0.
Attributes
hyperparameter_ranges
{}
model_family
ModelFamily.BASELINE
modifies_features
True
modifies_target
False
name
Baseline 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. Since baseline regressors do not use input features to calculate predictions, returns an array of zeroes.
Fits baseline regression component to data.
Find the prediction intervals using the fitted regressor.
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 the baseline regression strategy.
Make probability estimates for labels.
Saves component at file path.
Updates the parameter dictionary of the component.
- 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. Since baseline regressors do not use input features to calculate predictions, returns an array of zeroes.
- Returns
An array of zeroes.
- Return type
np.ndarray (float)
- fit(self, X, y=None)[source]#
Fits baseline regression component to data.
- Parameters
X (pd.DataFrame) – The input training data of shape [n_samples, n_features].
y (pd.Series) – The target training data of length [n_samples].
- Returns
self
- Raises
ValueError – If input y is None.
- get_prediction_intervals(self, X: pandas.DataFrame, y: Optional[pandas.Series] = None, coverage: List[float] = None, predictions: pandas.Series = None) Dict[str, pandas.Series]#
Find the prediction intervals using the fitted regressor.
This function takes the predictions of the fitted estimator and calculates the rolling standard deviation across all predictions using a window size of 5. The lower and upper predictions are determined by taking the percent point (quantile) function of the lower tail probability at each bound multiplied by the rolling standard deviation.
- Parameters
X (pd.DataFrame) – Data of shape [n_samples, n_features].
y (pd.Series) – Target data. Ignored.
coverage (list[float]) – A list of floats between the values 0 and 1 that the upper and lower bounds of the prediction interval should be calculated for.
predictions (pd.Series) – Optional list of predictions to use. If None, will generate predictions using X.
- Returns
Prediction intervals, keys are in the format {coverage}_lower or {coverage}_upper.
- Return type
dict
- Raises
MethodPropertyNotFoundError – If the estimator does not support Time Series Regression as a problem type.
- 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)[source]#
Make predictions using the baseline regression strategy.
- Parameters
X (pd.DataFrame) – Data of shape [n_samples, n_features].
- Returns
Predicted values.
- Return type
pd.Series
- predict_proba(self, X: pandas.DataFrame) pandas.Series#
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.
- update_parameters(self, update_dict, reset_fit=True)#
Updates the parameter dictionary of the component.
- Parameters
update_dict (dict) – A dict of parameters to update.
reset_fit (bool, optional) – If True, will set _is_fitted to False.