Packaged: 2019-02-13 15:10:14 UTC; jtrecenti

person("José", "Jesus", email = "jjesusfilho@gmail.com", role = "ctb"),
person("Dean", "Attali", email = "daattali@gmail.com", role = "ctb"),
person("C", "Lente", email = "c@lente.dev", role = "ctb"))
Imports: httr, shiny, shinyjs, htmltools, yaml, utils

Title: Automatic Variable Reduction Using Principal Component Analysis
Description: PCA done by eigenvalue decomposition of a data correlation matrix, here it automatically determines the number of factors by eigenvalue greater than 1 and it gives the uncorrelated variables based on the rotated component scores, Such that in each principal component variable which has the high variance are selected. It will be useful for non-statisticians in selection of variables. For more information, see the <http://www.ijcem.org/papers032013/ijcem_032013_06.pdf> web page.

Title: Learning to Rank Bagging Workflows with Metalearning
Authors@R: c(person("Fabio", "Pinto", role = c("aut")),
person("Vitor", "Cerqueira", email = "cerqueira.vitormanuel@gmail.com", role = "cre"),
person("Carlos", "Soares", role = "ctb"),
person("Joao", "Mendes-Moreira", role = "ctb"))
Description: A framework for automated machine learning. Concretely, the focus is on the optimisation of bagging workflows. A bagging workflows is composed by three phases: (i) generation: which and how many predictive models to learn; (ii) pruning: after learning a set of models, the worst ones are cut off from the ensemble; and (iii) integration: how the models are combined for predicting a new observation. autoBagging optimises these processes by combining metalearning and a learning to rank approach to learn from metadata. It automatically ranks 63 bagging workflows by exploiting past performance and dataset characterization. A complete description of the method can be found in: Pinto, F., Cerqueira, V., Soares, C., Mendes-Moreira, J. (2017): "autoBagging: Learning to Rank Bagging Workflows with Metalearning" arXiv preprint arXiv:1706.09367.
Imports: cluster, xgboost, methods, e1071, rpart, abind, caret, MASS,
entropy, lsr, CORElearn, infotheo, minerva, party