class_docstr: A predictor for scikit-learn compatible estimators.
  
  Args:
      estimator: The fitted scikit-learn compatible estimator to use for
          predictions.
      preprocessor: A preprocessor used to transform data batches prior
          to prediction.
class_docstr_tok: ["A","predictor","for","scikit-learn","compatible","estimators",".","Args",":","estimator",":","The","fitted","scikit-learn","compatible","estimator","to","use","for","predictions",".","preprocessor",":","A","preprocessor","used","to","transform","data","batches","prior","to","prediction","."]
code: def __init__(
      self,
      estimator: BaseEstimator,
      preprocessor: Optional["Preprocessor"] = None,
  ):
      self.estimator = estimator
      super().__init__(preprocessor)
  
file_path: ray/python/ray/train/sklearn/sklearn_predictor.py
imports: from typing import TYPE_CHECKING, List, Optional, Union
  import pandas
  from joblib import parallel_backend
  from sklearn.base import BaseEstimator
  from ray.air.constants import TENSOR_COLUMN_NAME
  from ray.air.data_batch_type import DataBatchType
  from ray.air.util.data_batch_conversion import _unwrap_ndarray_object_type_if_needed
  from ray.train.predictor import Predictor
  from ray.train.sklearn import SklearnCheckpoint
  from ray.train.sklearn._sklearn_utils import _set_cpu_params
  from ray.util.annotations import PublicAPI
  from ray.util.joblib import register_ray
label_desc: Use image node_id 1 to create a new SklearnPredictor object from inherited base classes: Predictor with example: obj = SklearnPredictor(estimator, preprocessor)