Description Usage Arguments Algorithms Examples

View source: R/splendid_model.R

Train, predict, and evaluate classification models

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`data` |
data frame with rows as samples, columns as features |

`class` |
true/reference class vector used for supervised learning |

`algorithms` |
character vector of algorithms to use for supervised
learning. See |

`n` |
number of bootstrap replicates to generate |

`seed_boot` |
random seed used for reproducibility in bootstrapping training sets for model generation |

`seed_alg` |
random seed used for reproducibility when running algorithms with an intrinsic random element (random forests) |

`convert` |
logical; if |

`rfe` |
logical; if |

`ova` |
logical; if |

`standardize` |
logical; if |

`plus` |
logical; if |

`threshold` |
a number between 0 and 1 indicating the lowest maximum class probability below which a sample will be unclassified. |

`trees` |
number of trees to use in "rf" or boosting iterations (trees) in "adaboost" |

`tune` |
logical; if |

The classification algorithms currently supported are:

Prediction Analysis for Microarrays ("pam")

Support Vector Machines ("svm")

Random Forests ("rf")

Linear Discriminant Analysis ("lda")

Shrinkage Linear Discriminant Analysis ("slda")

Shrinkage Diagonal Discriminant Analysis ("sdda")

Multinomial Logistic Regression using

Generalized Linear Model with no penalization ("mlr_glm")

GLM with LASSO penalty ("mlr_lasso")

GLM with ridge penalty ("mlr_ridge")

Neural Networks ("mlr_nnet")

Neural Networks ("nnet")

Naive Bayes ("nbayes")

Adaptive Boosting ("adaboost")

AdaBoost.M1 ("adaboost_m1")

Extreme Gradient Boosting ("xgboost")

K-Nearest Neighbours ("knn")

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AlineTalhouk/splendid documentation built on Aug. 30, 2018, 7:54 a.m.

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