• SINTEF

  • Referenced in 38 articles [sw02984]
  • application.Ecoplan is a decision support tool for long-term forest treatment scheduling. It is developed...
  • RTextTools

  • Referenced in 3 articles [sw21193]
  • classification (svm, slda, boosting, bagging, random forests, glmnet, decision trees, neural networks, maximum entropy), comprehensive...
  • Rborist

  • Referenced in 4 articles [sw23468]
  • Parallelizable Implementation of the Random Forest Algorithm. Scalable decision tree training and prediction...
  • RaSEn

  • Referenced in 3 articles [sw33917]
  • nearest neighbour, logistic regression, decision trees, random forest, support vector machines. RaSE algorithm can also...
  • HIBITS

  • Referenced in 4 articles [sw30626]
  • ordinal data, gradient boosting, decision tree and random forest. Using our proposed model, we show...
  • ForestSim

  • Referenced in 1 article [sw28010]
  • nuanced aspects of non-industrial private forest owner decision-making, forest growth dynamics, forest management...
  • Comet

  • Referenced in 3 articles [sw22899]
  • large-scale data. It builds multiple random forest ensembles on distributed blocks of data ... training subset for each decision tree in the random forest. Experiments with two large datasets...
  • ThunderGBM

  • Referenced in 1 article [sw39508]
  • Fast GBDTs and Random Forests on GPUs. Gradient Boosting Decision Trees (GBDTs) and Random Forests...
  • FARSITE

  • Referenced in 28 articles [sw31762]
  • FARSITE is widely used by the U.S. Forest Service, National Park Service, and other federal ... used for making fire and land management decisions. ** FARSITE4 will no longer be supported...
  • T3C

  • Referenced in 3 articles [sw23234]
  • evaluates T3C, a classification algorithm that builds decision trees of depth at most three ... C4.5, and competitive results compared to Random Forest and Rotation Forest...
  • sirus

  • Referenced in 2 articles [sw36843]
  • regression and classification algorithm based on random forests, which takes the form of a short ... simplicity of decision trees with a predictivity close to random forests. The core aggregation principle...
  • TreeInterpreter

  • Referenced in 1 article [sw33647]
  • interpreting scikit-learn’s decision tree and random forest predictions. Allows decomposing each prediction into...
  • iBitter-SCM

  • Referenced in 1 article [sw40046]
  • nearest neighbor, naive Bayes, decision tree and random forest) owing to its simplicity, interpretability...
  • RainForest

  • Referenced in 17 articles [sw20993]
  • RainForest — A Framework for Fast Decision Tree Construction of Large Datasets. Classification of large datasets ... present a unifying framework called Rain Forest for classification tree construction that separates the scalability...
  • diversityForest

  • Referenced in 1 article [sw41805]
  • binary splitting [2]. Interaction forests (IFs) are ensembles of decision trees that model quantitative ... author: Marvin N. Wright) that implements random forests using an efficient C++ implementation. References...
  • Tecton

  • Referenced in 9 articles [sw28905]
  • structured form called a proof forest; displaying them in an easy to comprehend form, using ... inference mechanisms, along with a linear arithmetic decision procedure. Further development of the system...
  • rminer

  • Referenced in 1 article [sw16693]
  • regression methods, including: Decision Trees, Neural Networks, Support Vector Machines, Random Forests, Bagging and Boosting...
  • psica

  • Referenced in 1 article [sw28008]
  • package psica: Decision Tree Analysis for Probabilistic Subgroup Identification with Multiple Treatments. In the situation ... computing random forests, and the resulting model is summarized by a decision tree in which...
  • StatPatternRecognition

  • Referenced in 1 article [sw15007]
  • discriminant analysis, decision trees, bump hunting (PRIM), boosting (AdaBoost), bagging and random forest algorithms...
  • SHAFF

  • Referenced in 1 article [sw41806]
  • random Forests. Interpretability of learning algorithms is crucial for applications involving critical decisions, and variable ... introduce SHAFF, SHApley eFfects via random Forests, a fast and accurate Shapley effect estimate, even...