Pages that link to "Item:Q1020835"

The following pages link to Unbiased split selection for classification trees based on the Gini index (Q1020835):

Displaying 40 items.

• All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously (Q97217) (← links)
• Evaluating the impact of a grouping variable on job satisfaction drivers (Q257557) (← links)
• Item-focussed trees for the identification of items in differential item functioning (Q316723) (← links)
• A toolbox of permutation tests for structural change (Q379927) (← links)
• Recursive partitioning on incomplete data using surrogate decisions and multiple imputation (Q434924) (← links)
• Rasch trees: a new method for detecting differential item functioning in the Rasch model (Q748201) (← links)
• A new variable importance measure for random forests with missing data (Q892440) (← links)
• A note on split selection bias in classification trees (Q956844) (← links)
• Variable selection bias in regression trees with constant fits (Q956860) (← links)
• An unbiased method for constructing multilabel classification trees (Q956995) (← links)
• Improving the precision of classification trees (Q965140) (← links)
• Unbiased variable selection for classification trees with multivariate responses (Q1010402) (← links)
• Maximally selected chi-squared statistics and non-monotonic associations: an exact approach based on two cutpoints (Q1020735) (← links)
• Editorial: Statistical learning methods including dimensionality reduction (Q1020825) (← links)
• Empirical characterization of random forest variable importance measures (Q1023556) (← links)
• A general splitting criterion for classification trees (Q1299513) (← links)
• Stochastic dominance with imprecise information (Q1621368) (← links)
• Variable selection by random forests using data with missing values (Q1623702) (← links)
• An alternative pruning based approach to unbiased recursive partitioning (Q1658507) (← links)
• Sensory analysis in the food industry as a tool for marketing decisions (Q1928204) (← links)
• Regression trees for longitudinal and multiresponse data (Q1951544) (← links)
• Evaluation of four multiple imputation methods for handling missing binary outcome data in the presence of an interaction between a dummy and a continuous variable (Q2039155) (← links)
• Tree-structured scale effects in binary and ordinal regression (Q2058717) (← links)
• Random forest with acceptance-rejection trees (Q2203396) (← links)
• Predicting missing values: a comparative study on non-parametric approaches for imputation (Q2282599) (← links)
• A new variable selection approach using random forests (Q2361222) (← links)
• Adaptive selection of extra cutpoints -- towards reconciling robustness and interpretability in classification trees (Q2431659) (← links)
• Three Steps Strategy to Search for Optimum Classification Trees (Q2809627) (← links)
• Variable selection for functional density trees (Q3168244) (← links)
• Appraisal of Performance of Three Tree-Based Classification Methods (Q4689247) (← links)
• (Q5053199) (← links)
• (Q5053217) (← links)
• Unbiased variable importance for random forests (Q5079869) (← links)
• Addressing the problem of missing data in decision tree modeling (Q5139013) (← links)
• (Q5149226) (← links)
• Model-based recursive partitioning algorithm to penalized non-crossing multiple quantile regression for the right-censored data (Q6050502) (← links)
• Efficient permutation testing of variable importance measures by the example of random forests (Q6113742) (← links)
• A tree-based modeling approach for matched case-control studies (Q6629956) (← links)
• Estimation of a predictor's importance by random forests when there is missing data: RISK prediction in liver surgery using laboratory data (Q6632702) (← links)
• A comparison of joint dichotomization and single dichotomization of interacting variables to discriminate a disease outcome (Q6637124) (← links)