Fiddler provides powerful visualizations to describe the impact of features in your model. Feature impact and importance can be found in either the Explain or Analyze tab.
Global explanations are available in the UI for structured (tabular) and natural language (NLP) models, for both classification and regression. They are also supported via API using the Fiddler Python package. Global explanations are available for both production and dataset queries.
For tabular models, Fiddler’s Global Explanation tool shows the impact/importance of the features in the model.
Two global explanation methods are available:
- Feature impact — Gives the average absolute change in the model prediction when a feature is randomly ablated (removed).
- Feature importance — Gives the average change in loss when a feature is randomly ablated.
For language models, Fiddler’s Global Explanation performs ablation feature impact on a collection of text samples, determining which words have the most impact on the prediction.
For speed performance, Fiddler uses a random corpus of 200 documents from the dataset. When using the
get_feature_importancefunction from the Fiddler API client, the argument
num_refscan be changed to use a bigger corpus of texts.
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Updated 3 months ago