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- W4387355700 abstract "We propose a simple generalization of standard and empirically successful decision tree learning algorithms such as ID3, C4.5, and CART. These algorithms, which have been central to machine learning for decades, are greedy in nature: they grow a decision tree by iteratively splitting on the best attribute. Our algorithm, Top-$k$, considers the $k$ best attributes as possible splits instead of just the single best attribute. We demonstrate, theoretically and empirically, the power of this simple generalization. We first prove a {sl greediness hierarchy theorem} showing that for every $k in mathbb{N}$, Top-$(k+1)$ can be dramatically more powerful than Top-$k$: there are data distributions for which the former achieves accuracy $1-varepsilon$, whereas the latter only achieves accuracy $frac1{2}+varepsilon$. We then show, through extensive experiments, that Top-$k$ outperforms the two main approaches to decision tree learning: classic greedy algorithms and more recent optimal decision tree algorithms. On one hand, Top-$k$ consistently enjoys significant accuracy gains over greedy algorithms across a wide range of benchmarks. On the other hand, Top-$k$ is markedly more scalable than optimal decision tree algorithms and is able to handle dataset and feature set sizes that remain far beyond the reach of these algorithms." @default.
- W4387355700 created "2023-10-05" @default.
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- W4387355700 date "2023-10-02" @default.
- W4387355700 modified "2023-10-06" @default.
- W4387355700 title "Harnessing the Power of Choices in Decision Tree Learning" @default.
- W4387355700 doi "https://doi.org/10.48550/arxiv.2310.01551" @default.
- W4387355700 hasPublicationYear "2023" @default.
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