• ranger

  • Referenced in 24 articles [sw14498]
  • forests for high dimensional data. Ensembles of classification, regression and survival trees are supported...
  • BayesTree

  • Referenced in 58 articles [sw07995]
  • Bayesian regression approach which uses dimensionally adaptive random basis elements. Motivated by ensemble methods ... point and interval estimates of the unknown regression function as well as the marginal effects...
  • SCPRED

  • Referenced in 6 articles [sw26876]
  • based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion: The SCPRED...
  • PLANET

  • Referenced in 5 articles [sw15434]
  • parallel learning of tree ensembles with mapreduce. Classification and regression tree learning on massive datasets ... construction of classification and regression trees, as well as ensembles of such models. We discuss...
  • ENDER

  • Referenced in 14 articles [sw12831]
  • constructs an ensemble of decision rules. This algorithm is tailored for regression and binary classification ... rules already present in the ensemble. We consider different loss functions and minimization techniques often...
  • CARTscans

  • Referenced in 2 articles [sw20892]
  • models, including (but not limited to) regression trees, ensembles of trees, and linear regressions with...
  • horserule

  • Referenced in 4 articles [sw27913]
  • package horserule: Tree ensembles with rule structured horseshoe regularization. We propose a new Bayesian ... model for flexible nonlinear regression and classification using tree ensembles. The model is based ... terms are used in a L1-regularized regression. We modify RuleFit by replacing ... which brings a desirable diversity to the ensemble. We sample from the posterior distribution using...
  • pycobra

  • Referenced in 2 articles [sw21382]
  • pycobra, a Python library devoted to ensemble learning (regression and classification) and visualisation. Its main...
  • GBMCI

  • Referenced in 2 articles [sw11476]
  • functions. Our nonparametric model utilizes an ensemble of regression trees to determine how the hazard ... varies according to the associated covariates. The ensemble model is trained using a gradient boosting...
  • R3P-Loc

  • Referenced in 2 articles [sw22444]
  • compact multi-label predictor using ridge regression and random projection for protein subcellular localization. Locating ... reduce the feature dimensions of an ensemble ridge regression (RR) classifier. Two new compact databases...
  • OTE

  • Referenced in 1 article [sw33152]
  • package OTE: Optimal Trees Ensembles for Regression, Classification and Class Membership Probability Estimation. Functions ... creating ensembles of optimal trees for regression, classification (Khan, Z., Gul, A., Perperoglou, A., Miftahuddin ... trees grown by random forest for the ensemble on the basis of their individual...
  • Quantregforest

  • Referenced in 5 articles [sw14249]
  • Quantile Regression Forests. Quantile Regression Forests is a tree-based ensemble method for estimation...
  • RandGA

  • Referenced in 3 articles [sw35808]
  • genetic algorithm for variable selection. Recently, the ensemble learning approaches have been proven ... selection in linear regression models. In general, a good variable selection ensemble should consist...
  • extraTrees

  • Referenced in 1 article [sw15907]
  • Classification and Regression. Classification and regression based on an ensemble of decision trees. The package ... ExtraTrees to multi-task learning and quantile regression. Uses Java implementation of the method...
  • PBoostGA

  • Referenced in 3 articles [sw17431]
  • consistently been a hot topic in linear regression models, especially when facing with high-dimensional ... variables are ranked suitably. In recent years, ensemble learning has gained a significant interest ... widespread success of boosting algorithms, a novel ensemble method PBoostGA is developed in this paper ... implement variable ranking and selection in linear regression models. In PBoostGA, a weight distribution...
  • SPMoE

  • Referenced in 2 articles [sw27748]
  • paper, we focus on modeling multi-target regression problems with high-dimensional feature spaces ... other ensemble models using three real cases of high-dimensional multi-target regression problems ... SPMoE is significantly better than the other ensemble and single models and can be considered ... modeling the high-dimensional multi-target regression problems...
  • SoilGrids250m

  • Referenced in 2 articles [sw27798]
  • ensemble of machine learning methods—random forest and gradient boosting and/or multinomial logistic regression ... fold cross-validation show that the ensemble models explain between 56% (coarse fragments ... machine learning instead of linear regression, (2) to considerable investments in preparing finer resolution covariate...
  • dtree

  • Referenced in 0 articles [sw19704]
  • decision tree algorithms, plus both linear regression and ensemble methods into one package. Allows...
  • SurvELM

  • Referenced in 0 articles [sw28066]
  • ELMBJEN), ELM with penalized Cox regression(ELMCox) and its ensemble (ELMCoxEN), ELM with graident boosting...
  • h2oEnsemble

  • Referenced in 1 article [sw33570]
  • This type of ensemble learning is called ”super learning”, ”stacked regression” or ”stacking.” The Super ... shown that the super learner ensemble represents an asymptotically optimal system for learning...