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FastTreeSHAP is a Python bundle that allows the environment friendly interpretation of tree-based machine studying fashions by computing sample-level characteristic significance values.2
The undertaking was not too long ago open-sourced by LinkedIn and was used on the firm to enhance member expertise in merchandise akin to Individuals You Could Know (PYMK), newsfeed rating, search, and job suggestions, in addition to customer-facing merchandise inside gross sales and advertising.
The FastTreeSHAP open-source bundle implements the algorithms FastTreeSHAP v1 and FastTreeSHAP v2 that make the packages 1.5x and a pair of.5x occasions quicker than TreeSHAP, respectively.
Parallel multi-core computing is absolutely enabled within the FastTreeSHAP bundle and it comprises the identical API because the TreeSHAP implementation within the SHAP bundle, excluding three further arguments that are straightforward to tune in apply.
“SHAP calculates the common influence of including a characteristic to the mannequin by accounting for all attainable subsets of the opposite options. In distinction to different approaches, SHAP has been justified as the one constant characteristic attribution strategy with a number of distinctive properties (native accuracy, missingness, and consistency), which agree with human instinct,” Jilei Yang, Humberto Gonzalez, Parvez Ahammad from LinkedIn wrote in a weblog put up. “As a result of its stable theoretical ensures, SHAP has change into a high mannequin interpretation strategy in business.”
After wanting into many TreeSHAP use circumstances, LinkedIn discovered that regardless of its algorithmic complexity enchancment, computing SHAP values for a big pattern measurement or a big mannequin measurement nonetheless remained a computational concern in apply. This resulted in the important thing enhancements within the FastTreeSHAP variations.
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