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","."]
docstr: Instantiate the predictor from a Checkpoint.
  
  The checkpoint is expected to be a result of ``SklearnTrainer``.
  
  Args:
      checkpoint: The checkpoint to load the model and
          preprocessor from. It is expected to be from the result of a
          ``SklearnTrainer`` run.
docstr_tok: ["Instantiate","the","predictor","from","a","Checkpoint",".","The","checkpoint","is","expected","to","be","a","result","of","``","SklearnTrainer","``",".","Args",":","checkpoint",":","The","checkpoint","to","load","the","model","and","preprocessor","from",".","It","is","expected","to","be","from","the","result","of","a","``","SklearnTrainer","``","run","."]
code: def from_checkpoint(
      cls, checkpoint: SklearnCheckpoint
  ) -> "SklearnPredictor":
      """Instantiate the predictor from a Checkpoint.
  
      The checkpoint is expected to be a result of ``SklearnTrainer``.
  
      Args:
          checkpoint: The checkpoint to load the model and
              preprocessor from. It is expected to be from the result of a
              ``SklearnTrainer`` run.
      """
      estimator = checkpoint.get_estimator()
      preprocessor = checkpoint.get_preprocessor()
      return cls(estimator=estimator, preprocessor=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 3 for calling the SklearnPredictor obj's underlying member method code with example usage: obj.from_checkpoint(cls, checkpoint) and returns: cls