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## FallbackPolicy Objects

### `classFallbackPolicy(Policy)`

Policy which predicts fallback actions.

A fallback can be triggered by a low confidence score on a NLU prediction or by a low confidence score on an action prediction.

#### `__init__`

```python
__init__(priority:int= FALLBACK_POLICY_PRIORITY, nlu_threshold:float= DEFAULT_NLU_FALLBACK_THRESHOLD, ambiguity_threshold:float= DEFAULT_NLU_FALLBACK_AMBIGUITY_THRESHOLD, core_threshold:float= DEFAULT_CORE_FALLBACK_THRESHOLD, fallback_action_name: Text = ACTION_DEFAULT_FALLBACK_NAME, **kwargs: Any) -> None
```

Create a new Fallback policy.

**Arguments**:

- `priority` - Fallback policy priority.
- `core_threshold` - if NLU confidence threshold is met, predict fallback action with confidence `core_threshold`. If this is the highest confidence in the ensemble, the fallback action will be executed.
- `nlu_threshold` - minimum threshold for NLU confidence. If intent prediction confidence is lower than this, predict fallback action with confidence 1.0.
- `ambiguity_threshold` - threshold for minimum difference between confidences of the top two predictions.
- `fallback_action_name` - name of the action to execute as a fallback.

#### `train`

```python
train(training_trackers: List[TrackerWithCachedStates], domain: Domain, interpreter: NaturalLanguageInterpreter, **kwargs: Any) -> None
```

Does nothing. This policy is deterministic.

#### `nlu_confidence_below_threshold`

```python
nlu_confidence_below_threshold(nlu_data: Dict[Text, Any]) -> Tuple[bool, float]
```

Check if the highest confidence is lower than `nlu_threshold`.

#### `nlu_prediction_ambiguous`

```python
nlu_prediction_ambiguous(nlu_data: Dict[Text, Any]) -> Tuple[bool, Optional[float]]
```

Check if top 2 confidences are closer than `ambiguity_threshold`.

#### `should_nlu_fallback`

```python
should_nlu_fallback(nlu_data: Dict[Text, Any], last_action_name: Text) -> bool
```

Check if fallback action should be predicted.

Checks for:

- predicted NLU confidence is lower than `nlu_threshold`
- difference in top 2 NLU confidences lower than `ambiguity_threshold`
- last action is action listen.

#### `fallback_scores`

```python
fallback_scores(domain: Domain, fallback_score: float=1.0) -> List[float]
```

Prediction scores used if a fallback is necessary.

#### `predict_action_probabilities`

```python
predict_action_probabilities(tracker: DialogueStateTracker, domain: Domain, interpreter: NaturalLanguageInterpreter, **kwargs: Any) -> PolicyPrediction
```

Predicts a fallback action.

The fallback action is predicted if the NLU confidence is low or no other policy has a high-confidence prediction.