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Found 5 papers, 1 papers with code
On the Relation between Prediction and Imputation Accuracy under Missing Covariates
In this work, we analyze through simulation the interaction between imputation accuracy and prediction accuracy in regression learning problems with missing covariates when Machine Learning based methods for both, imputation and prediction are used.
Interpretable Machines: Constructing Valid Prediction Intervals with Random Forests
An important issue when using Machine Learning algorithms in recent research is the lack of interpretability.
Universal Approximation Theorems of Fully Connected Binarized Neural Networks
Neural networks (NNs) are known for their high predictive accuracy in complex learning problems.
Asymptotic Unbiasedness of the Permutation Importance Measure in Random Forest Models
Due to its intuitive idea and flexible usage, it is important to explore circumstances, for which the permutation importance based on Random Forest correctly indicates informative covariates.
Who wins the Miss Contest for Imputation Methods? Our Vote for Miss BooPF
In this paper we study whether this approach can even be enhanced by other methods such as the stochastic gradient tree boosting method, the C5. 0 algorithm or modified random forest procedures.
