text: """
  Returns ML models from the MLLib library.
  """
  import pyspark
  from pyspark.ml.regression import LinearRegression
  from pyspark.ml.regression import DecisionTreeRegressor
  from pyspark.ml.regression import RandomForestRegressor
  from pyspark.ml.regression import GBTRegressor
  from pyspark.ml.classification import LogisticRegression
  from pyspark.ml.classification import DecisionTreeClassifier
  from pyspark.ml.classification import RandomForestClassifier
  from pyspark.ml.classification import GBTClassifier
  from pyspark.ml.classification import MultilayerPerceptronClassifier
  from pyspark.ml.classification import LinearSVC
  
  def baseModel(model="LinearRegression", labelCol="label", model_options={}):
      
      if model == "LinearRegression":
          return LinearRegression(featuresCol="features", labelCol=labelCol, **model_options) 
      elif model == "DecisionTreeRegressor":
          return DecisionTreeRegressor(featuresCol="features", labelCol=labelCol, **model_options)
      elif model == "RandomForestRegressor":
          return RandomForstRegressor(featuresCol="features", labelCol=labelCol, **model_options)
      elif model == "GradientBoostedTreeRegressor":
          return GBTRegressor(featuresCol="features", labelCol=labelCol, **model_options)
      elif model == "LogisticRegression":
          return LogisticRegression(featuresCol="features", labelCol=labelCol, **model_options)
      elif model == "DecisionTreeClassifier":
          return DecisionTreeClassifier(featuresCol="features", labelCol=labelCol, **model_options)
      elif model == "RandomForestClassifier":
          return RandomForestClassifier(featuresCol="features", labelCol=labelCol, **model_options)
      elif model == "GradientBoostedTreeClassifier":
          return GBTClassifier(featuresCol="features", labelCol=labelCol, **model_options)
      elif model == "MultilayerPerceptronClassifier":
          return MultilayerPerceptronClassifier(featuresCol="features", labelCol=labelCol, **model_options)
      elif model == "LinearSVM":
          return LinearSVC(featuresCol="features", labelCol=labelCol, **model_options)
  
  