class_docstr: Represents a Spark dataset (e.g. data derived from a Spark Table / file directory or Delta
  Table) for use with MLflow Tracking.
class_docstr_tok: ["Represents","a","Spark","dataset","(","e.g",".","data","derived","from","a","Spark","Table","\/","file","directory","or","Delta","Table",")","for","use","with","MLflow","Tracking","."]
docstr: Converts the dataset to an EvaluationDataset for model evaluation. Required
  for use with mlflow.evaluate().
docstr_tok: ["Converts","the","dataset","to","an","EvaluationDataset","for","model","evaluation",".","Required","for","use","with","mlflow.evaluate","(",")","."]
code: def to_evaluation_dataset(
      self, path=None, feature_names=None
  ) -> EvaluationDataset:
      """
      Converts the dataset to an EvaluationDataset for model evaluation. Required
      for use with mlflow.evaluate().
      """
      return EvaluationDataset(
          data=self._df.limit(10000).toPandas(),
          targets=self._targets,
          path=path,
          feature_names=feature_names,
      )
  
imports: import json
  import logging
  from functools import cached_property
  from typing import TYPE_CHECKING, Any, Dict, Optional, Union
  from mlflow.data.dataset import Dataset
  from mlflow.data.dataset_source import DatasetSource
  from mlflow.data.delta_dataset_source import DeltaDatasetSource
  from mlflow.data.digest_utils import compute_spark_df_digest
  from mlflow.data.pyfunc_dataset_mixin import PyFuncConvertibleDatasetMixin, PyFuncInputsOutputs
  from mlflow.data.spark_dataset_source import SparkDatasetSource
  from mlflow.exceptions import MlflowException
  from mlflow.models.evaluation.base import EvaluationDataset
  from mlflow.protos.databricks_pb2 import INTERNAL_ERROR, INVALID_PARAMETER_VALUE
  from mlflow.types import Schema
  from mlflow.types.utils import _infer_schema
  from mlflow.utils.annotations import experimental
label_desc: Use image node_id 10 for calling the SparkDataset obj's underlying member method code with example usage: obj.to_evaluation_dataset(path, feature_names) and returns: EvaluationDataset