Scaling optimal classification trees via adaptive feature and sample reduction via weighted STreeD reduces sample-dependent computation in fixed-candidate optimization
Read the original at arxiv.org→arXiv:2609.05826v1 Announce Type: new Abstract: Dynamic programming for optimal classification trees becomes computationally expensive as the numbers of features and training samples increase. We develop a joint...
Original headline: "Scaling Optimal Classification Trees via Adaptive Feature and Sample Reduction"
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- Sep 9, 04:00 UTC arXiv cs.LG lead source Scaling Optimal Classification Trees via Adaptive Feature and Sample Reduction