Explaining predictions¶

It is practical to understand your model. There are various ways how to gain some insights about your model behavior, see for example excellent Dan Becker’s tutorial on Kaggle: https://www.kaggle.com/learn/machine-learning-explainability .

Vowpal Wabbit offers various options how to inspect learned weights, subwabbit helps with use of audit mode. It allows to easily compute which features contributes the most for particular line’s prediction.

How to explain prediction¶

At first, you need to turn on audit_mode by passing audit_mode=True argument to subwabbit.base.VowpalWabbitBaseModel constructor.

Warning

When audit mode is turned on, it is not possible to call predict() and train() methods.

Then use explain_vw_line() to retrieve explanation string. It will look like this: c^c8*f^f10237121819548268936:23365229:1:0.0220863@0 a^a3426538138935958091*e^e115:1296634:0.2:0.0987504@0

Features used for prediction are separated by tab and for each feature, there is string in format: namespace^feature:hashindex:value:weight[@ssgrad]

Then we can use get_human_readable_explanation() function to transform explanation string into more interpretable structure:

subwabbit.base.VowpalWabbitBaseFormatter.get_human_readable_explanation(self, explanation_string: str, feature_translator: Any = None) → List[Dict[KT, VT]]

Transform explanation string into more readable form. Every feature used for prediction is translated into this structure:

{
    # For each feature used in higher interaction there is a 2-tuple
    'names': [('Human readable namespace name 1', 'Human readable feature name 1'), ...],
    'original_feature_name': 'c^c8*f^f102'  # feature name how vowpal sees it,
    'hashindex': 123,  # Vowpal's internal hash of feature name
    'value': 0.123, # value for feature in input line
    'weight': -0.534, # weight learned by VW for this feature
    'potential': value * weight,
    'relative_potential': abs(potential) / sum_of_abs_potentials_for_all_features
}

Parameters:	explanation_string – Explanation string from `explain_vw_line()` feature_translator – Any object that can help you with translation of feature names into human readable form, for example some database connection. See `parse_element()`
Returns:	List of dicts, sorted by contribution to final score

You may also want to overwrite parse_element() method on your formatter to translate Vowpal feature names into human readable form, for example translate IDs to their names, potentialy using some mapping in database.

Example¶

Feature importances can also be visualized in Jupyter notebook, see complete example of how to use subwabbit for explaining predictions:

pip install jupyter
jupyter notebook examples/Explaining-prediction.ipynb

Notes¶

Note

This explanation is valid if you use sparse features, since expected value of every feature is close to zero. When you use dense features, you should normalize your features. If you do not normalize to zero mean, explaining features by their absolute contribution is not informative because you also need to consider how feature value differs from some expected value of that feature. In this case, you should use SHAP values for better interpretability, see https://www.kaggle.com/learn/machine-learning-explainability for more details. You still may find subwabbit explaining functionality useful, but interpreting results results won’t be straightforward.

Note

In case you have correlated features, it is better to sum their potentials and consider them as single feature, otherwise you may underestimate influence of these features.