mailitics

Tag: trees

  • LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees

    LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees arXiv:2602.22432v1 Announce Type: new Abstract: Gradient-boosted decision trees are among the strongest off-the-shelf predictors for tabular regression, but point predictions alone do not quantify uncertainty. Conformal prediction provides distribution-free marginal coverage, yet split conformal uses a single global residual quantile and can be poorly adaptive under…

  • A Visual Guide to Tuning Gradient Boosted Trees

    A Visual Guide to Tuning Gradient Boosted Trees Introduction My previous posts looked at the bog-standard decision tree and the wonder of a random forest. Now, to complete the triplet, I’ll visually explore gradient boosted trees! There are a bunch of gradient boosted tree libraries, including XGBoost, CatBoost, and LightGBM. However, for this I’m going…

  • 3 Greedy Algorithms for Decision Trees, Explained with Examples

    3 Greedy Algorithms for Decision Trees, Explained with Examples Learn the inner workings of decision trees The post 3 Greedy Algorithms for Decision Trees, Explained with Examples appeared first on Towards Data Science. Kuriko Iwai Go to original source

  • Decision Trees Natively Handle Categorical Data

    Decision Trees Natively Handle Categorical Data But mean target encoding is their turbocharger The post Decision Trees Natively Handle Categorical Data appeared first on Towards Data Science. Vadim Arzamasov Go to original source

  • How to Set the Number of Trees in Random Forest

    How to Set the Number of Trees in Random Forest Scientific publication T. M. Lange, M. Gültas, A. O. Schmitt & F. Heinrich (2025). optRF: Optimising random forest stability by determining the optimal number of trees. BMC bioinformatics, 26(1), 95. Follow this LINK to the original publication. Random Forest — A Powerful Tool for Anyone…

  • Understanding Random Forest using Python (scikit-learn)

    Understanding Random Forest using Python (scikit-learn) Decision trees are a popular supervised learning algorithm with benefits that include being able to be used for both regression and classification as well as being easy to interpret. However, decision trees aren’t the most performant algorithm and are prone to overfitting due to small variations in the training…

  • Variational phylogenetic inference with products over bipartitions

    Variational phylogenetic inference with products over bipartitions arXiv:2502.15110v1 Announce Type: new Abstract: Bayesian phylogenetics requires accurate and efficient approximation of posterior distributions over trees. In this work, we develop a variational Bayesian approach for ultrametric phylogenetic trees. We present a novel variational family based on coalescent times of a single-linkage clustering and derive a closed-form…

  • Towards understanding the bias in decision trees

    Towards understanding the bias in decision trees arXiv:2501.04903v1 Announce Type: new Abstract: There is a widespread and longstanding belief that machine learning models are biased towards the majority (or negative) class when learning from imbalanced data, leading them to neglect or ignore the minority (or positive) class. In this study, we show that this belief…