baseline_classifier#

Baseline classifier.

Module Contents#

Classes Summary#

BaselineClassifier

Classifier that predicts using the specified strategy.

Contents#

class evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier(strategy='mode', random_seed=0, **kwargs)[source]#

Classifier that predicts using the specified strategy.

This is useful as a simple baseline classifier to compare with other classifiers.

Parameters

strategy (str) – Method used to predict. Valid options are “mode”, “random” and “random_weighted”. Defaults to “mode”.
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 Classifier
supported_problem_types	[ProblemTypes.BINARY, ProblemTypes.MULTICLASS]
training_only	False

Methods

`classes_`	Returns class labels. Will return None before fitting.
`clone`	Constructs a new component with the same parameters and random state.
`default_parameters`	Returns the default parameters for this component.
`describe`	Describe a component and its parameters.
`feature_importance`	Returns importance associated with each feature. Since baseline classifiers do not use input features to calculate predictions, returns an array of zeroes.
`fit`	Fits baseline classifier component to data.
`get_prediction_intervals`	Find the prediction intervals using the fitted regressor.
`load`	Loads component at file path.
`needs_fitting`	Returns boolean determining if component needs fitting before calling predict, predict_proba, transform, or feature_importances.
`parameters`	Returns the parameters which were used to initialize the component.
`predict`	Make predictions using the baseline classification strategy.
`predict_proba`	Make prediction probabilities using the baseline classification strategy.
`save`	Saves component at file path.
`update_parameters`	Updates the parameter dictionary of the component.

property classes_(self)#

Returns class labels. Will return None before fitting.

Returns: Class names
Return type: list[str] or list(float)

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 classifiers do not use input features to calculate predictions, returns an array of zeroes.

Returns: An array of zeroes
Return type: pd.Series

fit(self, X, y=None)[source]#

Fits baseline classifier 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 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 classification strategy.

Parameters: X (pd.DataFrame) – Data of shape [n_samples, n_features].
Returns: Predicted values.
Return type: pd.Series

predict_proba(self, X)[source]#

Make prediction probabilities using the baseline classification strategy.

Parameters: X (pd.DataFrame) – Data of shape [n_samples, n_features].
Returns: Predicted probability values.
Return type: pd.DataFrame

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.