# Dealing with a MissingMetadataException

## The problem

Running the `fit`

, `calculate`

or `estimate`

methods of a `Calculator`

or `Estimator`

fails by returning
a `MissingMetadataException`

.

```
nannyml.exceptions.MissingMetadataException: metadata is still missing values for ['predicted_probability_column_name'].
```

## The solution

The `MissingMetadataException`

is raised when the `model metadata`

used to create the `Calculator`

or `Estimator`

is not complete, i.e. it is missing some required properties.

The exception will list the properties it is missing, as shown in the problem statement.

Assume `md`

is the `model metadata`

object used,
`predicted_probability_column_name`

is the property missing and in your data the predicted probabilities are located
in the `model_probas`

column.

The following snippet should help you prevent the exception by completing the metadata manually:

```
>>> md.is_complete() # just checking
(False, ['predicted_probability_column_name'])
>>> md.predicted_probability_column_name = 'model_probas'
>>> md.is_complete()
(True, [])
```

Any metadata property can be set or updated.