class_docstr: Intent classifier using the sklearn framework.
class_docstr_tok: ["Intent","classifier","using","the","sklearn","framework","."]
docstr: The component's default config (see parent class for full docstring).
docstr_tok: ["The","component","'s","default","config","(","see","parent","class","for","full","docstring",")","."]
code: def get_default_config() -> Dict[Text, Any]:
      """The component's default config (see parent class for full docstring)."""
      return {
          # C parameter of the svm - cross validation will select the best value
          "C": [1, 2, 5, 10, 20, 100],
          # gamma parameter of the svm
          "gamma": [0.1],
          # the kernels to use for the svm training - cross validation will
          # decide which one of them performs best
          "kernels": ["linear"],
          # We try to find a good number of cross folds to use during
          # intent training, this specifies the max number of folds
          "max_cross_validation_folds": 5,
          # Scoring function used for evaluating the hyper parameters
          # This can be a name or a function (cfr GridSearchCV doc for more info)
          "scoring_function": "f1_weighted",
          "num_threads": 1,
      }
  
imports: from __future__ import annotations
  import logging
  from rasa.nlu.featurizers.dense_featurizer.dense_featurizer import DenseFeaturizer
  import typing
  import warnings
  from typing import Any, Dict, List, Optional, Text, Tuple, Type
  import numpy
  import rasa.shared.utils.io
  import rasa.utils.io
  from rasa.engine.graph import GraphComponent, ExecutionContext
  from rasa.engine.recipes.default_recipe import DefaultV1Recipe
  from rasa.engine.storage.resource import Resource
  from rasa.engine.storage.storage import ModelStorage
  from rasa.shared.constants import DOCS_URL_TRAINING_DATA_NLU
  from rasa.nlu.classifiers import LABEL_RANKING_LENGTH
  from rasa.shared.exceptions import RasaException
  from rasa.shared.nlu.constants import TEXT
  from rasa.nlu.classifiers.classifier import IntentClassifier
  from rasa.shared.nlu.training_data.training_data import TrainingData
  from rasa.shared.nlu.training_data.message import Message
  from rasa.utils.tensorflow.constants import FEATURIZERS
label_desc: Use image node_id 2 for calling the SklearnIntentClassifier obj's underlying member method code with example usage: obj.get_default_config() and returns: dict