classification: classification

In elpidiofilho/labgeo: Collection of functions to fit models with emphasis in land use and soil mapping

Description

This function performs the training of the chosen classifier

Usage

classification(df.train, formula = NULL, preprocess = NULL,
  classifier = "rf", rsample = "cv", nfolds = 10, repeats = NA,
  index = NULL, cpu_cores = 4, tune_length = 5, search = "grid",
  metric = "Kappa", seeds = NULL, verbose = FALSE)

Arguments

df.train	Training dataframe
formula	A formula of the form y ~ x1 + x2 + ... If users don't inform formula, the first column will be used as Y values and the others columns with x1,x2....xn
preprocess	pre process
classifier	Choice of classifier to be used to train model. Uses algortims names from Caret package.
nfolds	Number of folds to be build in crossvalidation
repeats	repeats
index	index
cpu_cores	Number of CPU cores to be used in parallel processing
tune_length	This argument is the number of levels for each tuning parameters that should be generated by train
search	search option "grid" or "random"
metric	metric used to evaluate model fit. For numeric outcome ("RMSE", "Rsquared)
seeds	seeds seeds
verbose	verbose

Details

details

Author(s)

Elpidio Filho, [e-mail]

Examples

## Not run: 
library(dplyr)
library(labgeo)

data("iris")
d = iris %>% select(Species, everything())
vt = train_test(df = d, p = 0.75,seed = 313)
train = vt$train
test = vt$test
fit = classification(df.train = train, preprocess = c('center', 'scale'),
                     classifier = 'rf', nfolds = 5, cpu_cores = 0,
                     metric = 'Kappa', tune_length = 3,
                     verbose = T )
pred = predict(fit, test)
obs = test[,1]
plot_confusion_matrix(obs, pred)

## End(Not run)

elpidiofilho/labgeo documentation built on May 14, 2019, 9:35 a.m.