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.
`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.

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.

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.

classifiers

catboost_classifier