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Found 8 papers, 4 papers with code
Randomization Can Reduce Both Bias and Variance: A Case Study in Random Forests
We study the often overlooked phenomenon, first noted in \cite{breiman2001random}, that random forests appear to reduce bias compared to bagging.
FAST: An Optimization Framework for Fast Additive Segmentation in Transparent ML
We present FAST, an optimization framework for fast additive segmentation.
FIRE: An Optimization Approach for Fast Interpretable Rule Extraction
We present FIRE, Fast Interpretable Rule Extraction, an optimization-based framework to extract a small but useful collection of decision rules from tree ensembles.
ControlBurn: Nonlinear Feature Selection with Sparse Tree Ensembles
ControlBurn is a Python package to construct feature-sparse tree ensembles that support nonlinear feature selection and interpretable machine learning.
ForestPrune: Compact Depth-Controlled Tree Ensembles
We present ForestPrune, a novel optimization framework to post-process tree ensembles by pruning depth layers from individual trees.
ControlBurn: Feature Selection by Sparse Forests
Like the linear LASSO, ControlBurn assigns all the feature importance of a correlated group of features to a single feature.
Impact of Accuracy on Model Interpretations
Model interpretations are often used in practice to extract real world insights from machine learning models.
Offline Handwritten Chinese Text Recognition with Convolutional Neural Networks
Deep learning based methods have been dominating the text recognition tasks in different and multilingual scenarios.
