class_docstr: (Private) Java Model for prediction tasks (regression and classification).
class_docstr_tok: ["(","Private",")","Java","Model","for","prediction","tasks","(","regression","and","classification",")","."]
docstr: Returns the number of features the model was trained on. If unknown, returns -1
docstr_tok: ["Returns","the","number","of","features","the","model","was","trained","on",".","If","unknown",",","returns","-1"]
code: def numFeatures(self) -> int:
      """
      Returns the number of features the model was trained on. If unknown, returns -1
      """
      return self._call_java("numFeatures")
  
imports: from abc import ABCMeta, abstractmethod
  from typing import Any, Generic, Optional, List, Type, TypeVar, TYPE_CHECKING
  from pyspark import since
  from pyspark import SparkContext
  from pyspark.sql import DataFrame
  from pyspark.ml import Estimator, Predictor, PredictionModel, Transformer, Model
  from pyspark.ml.base import _PredictorParams
  from pyspark.ml.param import Param, Params
  from pyspark.ml.util import _jvm
  from pyspark.ml.common import inherit_doc, _java2py, _py2java
label_desc: Use image node_id 1 for calling the JavaPredictionModel obj's underlying member method code with example usage: obj.numFeatures() and returns: self