## LogisticRegressionClassifier Objects

### LogisticRegressionClassifier

```python
@DefaultV1Recipe.register(
    DefaultV1Recipe.ComponentType.INTENT_CLASSIFIER, is_trainable=True
)
class LogisticRegressionClassifier(IntentClassifier, GraphComponent):
    """Intent classifier using the Logistic Regression."""
```

### required_components

```python
@classmethod
def required_components(cls) -> List[Type]:
    """Components that should be included in the pipeline before this component."""
```

### required_packages

```python
@staticmethod
def required_packages() -> List[Text]:
    """Any extra python dependencies required for this component to run."""
```

### get_default_config

```python
@staticmethod
def get_default_config() -> Dict[Text, Any]:
    """The component's default config (see parent class for full docstring)."""
```

### __init__

```python
def __init__(config: Dict[Text, Any], name: Text, model_storage: ModelStorage,
             resource: Resource) -> None:
    """Construct a new classifier."""
```

### train

```python
def train(training_data: TrainingData) -> Resource:
    """Train the intent classifier on a data set."""
```

### create

```python
@classmethod
def create(
    cls, config: Dict[Text, Any], model_storage: ModelStorage,
    resource: Resource,
    execution_context: ExecutionContext) -> "LogisticRegressionClassifier":
    """Creates a new untrained component (see parent class for full docstring)."""
```

### process

```python
def process(messages: List[Message]) -> List[Message]:
    """Return the most likely intent and its probability for a message."""
```

### persist

```python
def persist() -> None:
    """Persist this model into the passed directory."""
```

### load

```python
@classmethod
def load(cls, config: Dict[Text, Any], model_storage: ModelStorage,
         resource: Resource, execution_context: ExecutionContext,
         **kwargs: Any) -> "LogisticRegressionClassifier":
    """Loads trained component (see parent class for full docstring)."""
```

### process_training_data

```python
def process_training_data(training_data: TrainingData) -> TrainingData:
    """Process the training data."""
```

### validate_config

```python
@classmethod
def validate_config(cls, config: Dict[Text, Any]) -> None:
    """Validates that the component is configured properly."""
```