Accelerate Your SHAP Processing time with FastTreeShap
SHAP is one of the most used techniques to explain our Machine Learning model.
For a tree-based ensemble model such as XGBoost and LightGBM, it’s a harder task to interpret the predictions result. That is why, one way to interpret the result is by using SHAP.
SHAP works by Interpreting the predictions of machine learning models by attributing a feature's contribution to a prediction by considering the feature's value in all possible combinations with other features.
However, SHAP is considered to be a computational task. It would take a longer time to calculate the result for larger datasets with bigger hyperparameters.
Luckily, the research was developed to accelerate the Tree-Based model SHAP computation called FastTreeSHAP.
Empirical research has shown that FastTreeSHAP is considerably faster with less memory usage than the original SHAP. Moreover, the API developed for the package was similar to SHAP; which users can generally take advantage of immediately.
That is all for today! Please comment if you want to know something else from Machine Learning and Python domain!