class_docstr: Automated Machine Learning for supervised tasks (binary classification, multiclass classification, regression).
class_docstr_tok: ["Automated","Machine","Learning","for","supervised","tasks","(","binary","classification",",","multiclass","classification",",","regression",")","."]
docstr: Calculates a goodness of `fit` for an AutoML instance.
  
  Arguments:
      X (numpy.ndarray or pandas.DataFrame):
          Test values to make predictions on.
  
      y (numpy.ndarray or pandas.Series):
          True labels for X.
  
      sample_weight (numpy.ndarray or pandas.Series):
          Sample weights.
  Returns:
      float: Returns a goodness of fit measure (higher is better):
  
      - For classification tasks: returns the mean accuracy on the given test data and labels.
      - For regression tasks: returns the R^2 (coefficient of determination) on the given test data and labels.
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code: def score(
      self,
      X: Union[numpy.ndarray, pandas.DataFrame],
      y: Optional[Union[numpy.ndarray, pandas.Series]] = None,
      sample_weight: Optional[
          Union[numpy.ndarray, pandas.Series]
      ] = None,
  ) -> float:
      """Calculates a goodness of `fit` for an AutoML instance.
  
      Arguments:
          X (numpy.ndarray or pandas.DataFrame):
              Test values to make predictions on.
  
          y (numpy.ndarray or pandas.Series):
              True labels for X.
  
          sample_weight (numpy.ndarray or pandas.Series):
              Sample weights.
      Returns:
          float: Returns a goodness of fit measure (higher is better):
  
          - For classification tasks: returns the mean accuracy on the given test data and labels.
          - For regression tasks: returns the R^2 (coefficient of determination) on the given test data and labels.
      """
      return self._score(X, y, sample_weight)
  
imports: import logging
  import matplotlib
  import warnings
  from collections.abc import Iterable
  from re import U
  from typing import List, Optional, Union
  import numpy
  import pandas
  from typing_extensions import Literal
  from supervised.base_automl import BaseAutoML
  from supervised.utils.config import LOG_LEVEL
label_desc: Use image node_id 6 for calling the AutoML obj's underlying member method code with example usage: obj.score(X, y, sample_weight) and returns: self