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This is documentation for Rasa Open Source Documentation v2.0.x, which is no longer actively maintained.
For up-to-date documentation, see the latest version (2.1.x).

Version: 2.0.x

rasa.core.policies.sklearn_policy

SklearnPolicy Objects

class SklearnPolicy(Policy)

Use an sklearn classifier to train a policy.

__init__

| __init__(featurizer: Optional[MaxHistoryTrackerFeaturizer] = None, priority: int = DEFAULT_POLICY_PRIORITY, max_history: int = DEFAULT_MAX_HISTORY, model: Optional["sklearn.base.BaseEstimator"] = None, param_grid: Optional[Dict[Text, List] or List[Dict]] = None, cv: Optional[int] = None, scoring: Optional[Text or List or Dict or Callable] = "accuracy", label_encoder: LabelEncoder = LabelEncoder(), shuffle: bool = True, zero_state_features: Optional[Dict[Text, List["Features"]]] = None, **kwargs: Any, ,) -> None

Create a new sklearn policy.

Arguments:

  • featurizer - Featurizer used to convert the training data into vector format.
  • model - The sklearn model or model pipeline.
  • param_grid - If param_grid is not None and cv is given, a grid search on the given param_grid is performed (e.g. param_grid={'n_estimators': [50, 100]}).
  • cv - If cv is not None, perform a cross validation on the training data. cv should then conform to the sklearn standard (e.g. cv=5 for a 5-fold cross-validation).
  • scoring - Scoring strategy, using the sklearn standard.
  • label_encoder - Encoder for the labels. Must implement an inverse_transform method.
  • shuffle - Whether to shuffle training data.
  • zero_state_features - Contains default feature values for attributes