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mlearning (version 1.2.1)

Machine Learning Algorithms with Unified Interface and Confusion Matrices

Description

A unified interface is provided to various machine learning algorithms like linear or quadratic discriminant analysis, k-nearest neighbors, random forest, support vector machine, ... It allows to train, test, and apply cross-validation using similar functions and function arguments with a minimalist and clean, formula-based interface. Missing data are processed the same way as base and stats R functions for all algorithms, both in training and testing. Confusion matrices are also provided with a rich set of metrics calculated and a few specific plots.

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Version

Install

install.packages('mlearning')

Monthly Downloads

229

Version

1.2.1

License

GPL (>= 2)

Maintainer

Philippe Grosjean

Last Published

August 30th, 2023

Functions in mlearning (1.2.1)

mlKnn

Supervised classification using k-nearest neighbor
mlNaiveBayes

Supervised classification using naive Bayes
mlNnet

Supervised classification and regression using neural network
mlQda

Supervised classification using quadratic discriminant analysis
mlSvm

Supervised classification and regression using support vector machine
mlRforest

Supervised classification and regression using random forest
confusion

Construct and analyze confusion matrices
mlRpart

Supervised classification and regression using recursive partitioning
mlLda

Supervised classification using linear discriminant analysis
mlLvq

Supervised classification using learning vector quantization
mlearning

Machine learning model for (un)supervised classification or regression
train

Get the training variable for a mlearning object
prior

Get or set priors on a confusion matrix
mlearning-package

Machine Learning Algorithms with Unified Interface and Confusion Matrices
plot.confusion

Plot a confusion matrix
response

Get the response variable for a mlearning object