## TrainingResult Objects

### class TrainingResult(NamedTuple)

Holds information about the results of training.

### get_unresolved_slots

```python

def get_unresolved_slots(domain: Domain, stories: StoryGraph) -> List[Text]:
    """
    Returns a list of unresolved slots.
    """
```
**Arguments**:

- `domain` - The domain.
- `stories` - The story graph.

**Returns**:

A list of unresolved slots.

### train

```python

def train(domain: Text,
           config: Text,
           training_files: Optional[Union[Text, List[Text]]],
           output: Text = rasa.shared.constants.DEFAULT_MODELS_PATH,
           dry_run: bool = False,
           force_training: bool = False,
           fixed_model_name: Optional[Text] = None,
           persist_nlu_training_data: bool = False,
           core_additional_arguments: Optional[Dict] = None,
           nlu_additional_arguments: Optional[Dict] = None,
           model_to_finetune: Optional[Text] = None,
           finetuning_epoch_fraction: float = 1.0) -> TrainingResult:
    """
    Trains a Rasa model (Core and NLU).
    """
```
**Arguments**:

- `domain` - Path to the domain file.
- `config` - Path to the config file.
- `training_files` - List of paths to training data files.
- `output` - Output directory for the trained model.
- `dry_run` - If `True` then no training will be done, and the information about whether the training needs to be done will be printed.
- `force_training` - If `True` retrain model even if data has not changed.
- `fixed_model_name` - Name of model to be stored.
- `persist_nlu_training_data` - `True` if the NLU training data should be persisted with the model.
- `core_additional_arguments` - Additional training parameters for core training.
- `nlu_additional_arguments` - Additional training parameters forwarded to training method of each NLU component.
- `model_to_finetune` - Optional path to a model which should be finetuned or a directory in case the latest trained model should be used.
- `finetuning_epoch_fraction` - The fraction currently specified training epochs in the model configuration which should be used for finetuning.

**Returns**:

An instance of `TrainingResult`.

### train_core

```python

def train_core(domain: Union[Domain, Text],
               config: Text,
               stories: Text,
               output: Text,
               fixed_model_name: Optional[Text] = None,
               additional_arguments: Optional[Dict] = None,
               model_to_finetune: Optional[Text] = None,
               finetuning_epoch_fraction: float = 1.0) -> Optional[Text]:
    """
    Trains a Core model.
    """
```
**Arguments**:

- `domain` - Path to the domain file.
- `config` - Path to the config file for Core.
- `stories` - Path to the Core training data.
- `output` - Output path.
- `fixed_model_name` - Name of model to be stored.
- `additional_arguments` - Additional training parameters.
- `model_to_finetune` - Optional path to a model which should be finetuned or a directory in case the latest trained model should be used.
- `finetuning_epoch_fraction` - The fraction currently specified training epochs in the model configuration which should be used for finetuning.

**Returns**:

Path to the model archive.

### train_nlu

```python

def train_nlu(config: Text,
               nlu_data: Optional[Text],
               output: Text,
               fixed_model_name: Optional[Text] = None,
               persist_nlu_training_data: bool = False,
               additional_arguments: Optional[Dict] = None,
               domain: Optional[Union[Domain, Text]] = None,
               model_to_finetune: Optional[Text] = None,
               finetuning_epoch_fraction: float = 1.0) -> Optional[Text]:
    """
    Trains an NLU model.
    """
```
**Arguments**:

- `config` - Path to the config file for NLU.
- `nlu_data` - Path to the NLU training data.
- `output` - Output path.
- `fixed_model_name` - Name of the model to be stored.
- `persist_nlu_training_data` - `True` if the NLU training data should be persisted with the model.
- `additional_arguments` - Additional training parameters which will be passed to the `train` method of each component.
- `domain` - Path to the optional domain file/Domain object.
- `model_to_finetune` - Optional path to a model which should be finetuned or a directory in case the latest trained model should be used.
- `finetuning_epoch_fraction` - The fraction currently specified training epochs in the model configuration which should be used for finetuning.

**Returns**:

Path to the model archive.