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Out of the box, these models take as input a sequence of vectors in embedding space and output asequence ofvectors inthe same space. We treat the prediction of the model at the position corresponding toxi (that is absolute position 2i 1)asthepredictionof f(xi). A.2 Training Each training prompt is produced by sampling a random functionf from the function class we are training on, then sampling inputsxi from the isotropic Gaussian distributionN(0,Id) and constructing apromptas(x1,f(x1),...,xk,f(xk)). For the class of decision trees, the random functionf is represented by a decision tree of depth4 (with16leafnodes),with20dimensionalinputs. Minimum norm least squares is the optimal estimator for the linear regression problem.

Datasets can be biased due to societal inequities, human biases, underrepresentation of minorities, etc.

Decision trees are well-known due to their ease of interpretability. To improve accuracy, we need to grow deep trees or ensembles of trees. These are hard to interpret, offsetting their original benefits. Shapley values have recently become a popular way to explain the predictions of tree-based machine learning models. It provides a linear weighting to features independent of the tree structure. The rise in popularity is mainly due to TreeShap, which solves a general exponential complexity problem in polynomial time. Following extensive adoption in the industry, more efficient algorithms are required. This paper presents a more efficient and straightforward algorithm: Linear TreeShap. Like TreeShap, Linear TreeShap is exact and requires the same amount of memory.

The results of our survey are shown in Table 10.