Tag
ensemble-learning
- Jul 29, 2026 Split-Time Feature Subsampling in Random Forests Restricting the candidate columns at every split lowers the correlation between trees, and the strength-correlation trade explains why the default is neither one feature nor all of them. machine learning
- Jul 21, 2026 Gradient Boosting as Gradient Descent in Function Space Each boosting round fits a small tree to the negative gradient of the loss at the current predictions, which makes the algorithm a steepest-descent step whose direction comes from a restricted model class. machine learning
- Aug 28, 2025 Bagging: Bootstrap Resampling and the Variance It Removes Averaging models trained on bootstrap resamples reduces variance only as far as the models are uncorrelated, which explains both the gains and the cases where there are none. machine learning