The following pages link to rpart (Q19171):
Displaying 50 items.
- Computational methods for asynchronous basins (Q727917) (← links)
- Poisson dependency networks: gradient boosted models for multivariate count data (Q747270) (← links)
- Imitation learning of car driving skills with decision trees and random forests (Q747406) (← links)
- Mathematical optimization in classification and regression trees (Q828748) (← links)
- Book review of: R. Gentleman et al., Bioinformatics and computational biology solutions using R and bioconductor (Q840953) (← links)
- Rationale and applications of survival tree and survival ensemble methods (Q888036) (← links)
- Statistical learning from a regression perspective (Q925090) (← links)
- Generalised indirect classifiers (Q957252) (← links)
- Estimating classification error rate: repeated cross-validation, repeated hold-out and bootstrap (Q961845) (← links)
- Improving the precision of classification trees (Q965140) (← links)
- Navigating random forests and related advances in algorithmic modeling (Q975577) (← links)
- Classification by ensembles from random partitions of high-dimensional data (Q1020719) (← links)
- Classification tree analysis using TARGET (Q1023462) (← links)
- Fatigue life evaluation of corroded structural steel members in boundary with concrete (Q1039228) (← links)
- EZtune (Q1334360) (← links)
- treemisc (Q1350040) (← links)
- Double-bagging: Combining classifiers by bootstrap aggregation (Q1402702) (← links)
- Combining non-parametric models with logistic regression: an application to motor vehicle injury data. (Q1583208) (← links)
- (Psycho-)analysis of benchmark experiments: a formal framework for investigating the relationship between data sets and learning algorithms (Q1621378) (← links)
- Recursive partitioning for missing data imputation in the presence of interaction effects (Q1623390) (← links)
- RHSBoost: improving classification performance in imbalance data (Q1654228) (← links)
- Should we impute or should we weight? Examining the performance of two CART-based techniques for addressing missing data in small sample research with nonnormal variables (Q1658370) (← links)
- Classification trees for poverty mapping (Q1658373) (← links)
- Deriving optimal data-analytic regimes from benchmarking studies (Q1658481) (← links)
- An alternative pruning based approach to unbiased recursive partitioning (Q1658507) (← links)
- \(l_1\) regularized multiplicative iterative path algorithm for non-negative generalized linear models (Q1659085) (← links)
- Classification tree methods for panel data using wavelet-transformed time series (Q1663123) (← links)
- Machine learning techniques for mortality modeling (Q1689019) (← links)
- Modeling threshold interaction effects through the logistic classification trunk (Q1695093) (← links)
- Big data: from collection to visualization (Q1699628) (← links)
- Nonparametric subgroup identification by PRIM and CART: a simulation and application study (Q1705332) (← links)
- Instance spaces for machine learning classification (Q1707470) (← links)
- Fitting multivariate responses using scalar trees (Q1771462) (← links)
- Deformation of log-likelihood loss function for multiclass boosting (Q1784701) (← links)
- Tree-based multivariate regression and density estimation with right-censored data (Q1876993) (← links)
- A review of survival trees (Q1950331) (← links)
- PPtree: projection pursuit classification tree (Q1951161) (← links)
- Weighted distance-based trees for ranking data (Q1999451) (← links)
- Benchmark for filter methods for feature selection in high-dimensional classification data (Q2008133) (← links)
- Cyber claim analysis using generalized Pareto regression trees with applications to insurance (Q2034155) (← links)
- Isotonic boosting classification rules (Q2036157) (← 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)
- Optimal decision trees for categorical data via integer programming (Q2046341) (← links)
- Big data time series forecasting based on pattern sequence similarity and its application to the electricity demand (Q2053793) (← links)
- Mosaic flows: a transferable deep learning framework for solving PDEs on unseen domains (Q2072515) (← links)
- Models under which random forests perform badly; consequences for applications (Q2095717) (← links)
- Regularized target encoding outperforms traditional methods in supervised machine learning with high cardinality features (Q2095774) (← links)
- Optimal survival trees (Q2102354) (← links)
- Partitioning around medoids clustering and random forest classification for GIS-informed imputation of fluoride concentration data (Q2135383) (← links)
- A Lasso and a regression tree mixed-effect model with random effects for the level, the residual variance, and the autocorrelation (Q2152403) (← links)
