Artifacts using CARET (6)

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It implements Freund and Schapire's Adaboost.M1 algorithm and Breiman's Bagging algorithm using classification trees as individual classifiers. Once these classifiers have been trained, they can be used to predict on new data. Also, cross validation estimation of the error can be done. Since version 2.0 the function "margins" is available to calculate the margins for these classifiers. Also a higher flexibility is achieved giving access to the "rpart.control" argument of "rpart". Four important new features ...
Last Release on Feb 15, 2021
Functions for classification and group membership probability estimation are given. The issue of non-informative features in the data is addressed by utilizing the ensemble method. A few optimal models are selected in the ensemble from an initially large set of base k-nearest neighbours (KNN) models, generated on subset of features from the training data. A two stage assessment is applied in selection of optimal models for the ensemble in the training function. The prediction functions for classification ...
Last Release on Apr 30, 2022
Multivariate Expectation-Maximization (EM) based imputation framework that offers several different algorithms. These include regularisation methods like Lasso and Ridge regression, tree-based models and dimensionality reduction methods like PCA and PLS.
Last Release on May 12, 2022
Check the power of a statistical model moving sample size k using half-Cross Validation.
Last Release on Apr 30, 2022
Distributed gradient boosting based on the mboost package. The parboost package is designed to scale up component-wise functional gradient boosting in a distributed memory environment by splitting the observations into disjoint subsets, or alternatively using bootstrap samples (bagging). Each cluster node then fits a boosting model to its subset of the data. These boosting models are combined in an ensemble, either with equal weights, or by fitting a (penalized) regression model on the predictions of the ...
Last Release on May 1, 2022
Provides a number of functions for carrying out inference with set-theoretic comparative methods, including facilities for examining causal paths, assessing the sensitivity of results to measurement and model specification error, and performing Random Forest Comparative Analysis.
Last Release on May 1, 2022
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