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This notebook introduces the Data-OOB method, an implementation based on a publication from Kwon and Zou "Data-OOB: Out-of-bag Estimate as a Simple and Efficient Data Value" ICML 2023 , using pyDVL.
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The objective of this paper is mainly to overcome the computational bottleneck of shapley-based data valuation methods that require to fit a significant number of models to accurately estimate marginal contributions.
+The algorithms computes data values from out of bag estimates using a bagging model.
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The value can be interpreted as a partition of the OOB estimate, which is originally introduced to estimate the prediction error. This OOB estimate is given as:
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\[
+\sum_{i=1}^n\frac{\sum_{b=1}^{B}\mathbb{1}(w_{bi}=0)T(y_i, \hat{f}_b(x_i))}{\sum_{b=1}^{B}
+\mathbb{1}
+(w_{bi}=0)}
+\]
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