baseline_classifier ================================================================================ .. py:module:: evalml.pipelines.components.estimators.classifiers.baseline_classifier .. autoapi-nested-parse:: Baseline classifier. Module Contents --------------- Classes Summary ~~~~~~~~~~~~~~~ .. autoapisummary:: evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier Contents ~~~~~~~~~~~~~~~~~~~ .. py:class:: BaselineClassifier(strategy='mode', random_seed=0, **kwargs) Classifier that predicts using the specified strategy. This is useful as a simple baseline classifier to compare with other classifiers. :param strategy: Method used to predict. Valid options are "mode", "random" and "random_weighted". Defaults to "mode". :type strategy: str :param random_seed: Seed for the random number generator. Defaults to 0. :type random_seed: int **Attributes** .. list-table:: :widths: 15 85 :header-rows: 0 * - **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** .. autoapisummary:: :nosignatures: evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.classes_ evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.clone evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.default_parameters evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.describe evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.feature_importance evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.fit evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.load evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.needs_fitting evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.parameters evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.predict evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.predict_proba evalml.pipelines.components.estimators.classifiers.baseline_classifier.BaselineClassifier.save .. py:method:: classes_(self) :property: Returns class labels. Will return None before fitting. :returns: Class names :rtype: list[str] or list(float) .. py:method:: 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. .. py:method:: 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. :rtype: dict .. py:method:: describe(self, print_name=False, return_dict=False) Describe a component and its parameters. :param print_name: whether to print name of component :type print_name: bool, optional :param return_dict: whether to return description as dictionary in the format {"name": name, "parameters": parameters} :type return_dict: bool, optional :returns: Returns dictionary if return_dict is True, else None. :rtype: None or dict .. py:method:: feature_importance(self) :property: 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 :rtype: pd.Series .. py:method:: fit(self, X, y=None) Fits baseline classifier component to data. :param X: The input training data of shape [n_samples, n_features]. :type X: pd.DataFrame :param y: The target training data of length [n_samples]. :type y: pd.Series :returns: self :raises ValueError: If y is None. .. py:method:: load(file_path) :staticmethod: Loads component at file path. :param file_path: Location to load file. :type file_path: str :returns: ComponentBase object .. py:method:: 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. .. py:method:: parameters(self) :property: Returns the parameters which were used to initialize the component. .. py:method:: predict(self, X) Make predictions using the baseline classification strategy. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :returns: Predicted values. :rtype: pd.Series .. py:method:: predict_proba(self, X) Make prediction probabilities using the baseline classification strategy. :param X: Data of shape [n_samples, n_features]. :type X: pd.DataFrame :returns: Predicted probability values. :rtype: pd.DataFrame .. py:method:: save(self, file_path, pickle_protocol=cloudpickle.DEFAULT_PROTOCOL) Saves component at file path. :param file_path: Location to save file. :type file_path: str :param pickle_protocol: The pickle data stream format. :type pickle_protocol: int