## SklearnIntentClassifier Objects

### `class SklearnIntentClassifier(IntentClassifier)`
Intent classifier using the sklearn framework.

### `__init__(component_config: Optional[Dict[Text, Any]]=None, clf:"sklearn.model_selection.GridSearchCV"=None, le: Optional["sklearn.preprocessing.LabelEncoder"]=None)->None`
Construct a new intent classifier using the sklearn framework.

### `transform_labels_str2num(labels: List[Text])-> np.ndarray`
Transforms a list of strings into numeric label representation.

**Arguments**:

- `labels`: List of labels to convert to numeric representation

### `transform_labels_num2str(y: np.ndarray)-> np.ndarray`
Transforms a list of strings into numeric label representation.

**Arguments**:

- `y`: List of labels to convert to numeric representation

### `train(training_data: TrainingData, config: Optional[RasaNLUModelConfig]=None, **kwargs: Any)->None`
Train the intent classifier on a data set.

### `process(message: Message, **kwargs: Any)->None`
Return the most likely intent and its probability for a message.

### `predict_prob(X: np.ndarray)-> np.ndarray`
Given a bow vector of an input text, predict the intent label.

**Returns**:

- Vector of probabilities containing one entry for each label

### `predict(X: np.ndarray)-> Tuple[np.ndarray, np.ndarray]`
Given a bow vector of an input text, predict most probable label.

**Returns**:

- Tuple of first, the most probable label and second,

### `persist(file_name: Text, model_dir: Text)-> Optional[Dict[Text, Any]]`
Persist this model into the passed directory.

### `load(cls, meta: Dict[Text, Any], model_dir: Text, model_metadata: Optional[Metadata]=None, cached_component: Optional["SklearnIntentClassifier"]=None, **kwargs: Any)->"SklearnIntentClassifier"`
Loads trained component (see parent class for full docstring).