suggest_accuracy: Compare performance based on accuracy metrics.

In modeval: Evaluation of Classification Model Options

Description

suggest_accuracy Compare performance for all model fits in the Summary list based on Accuracy and Kappa metrics, together with training time for a single tuning of each model fit.

Usage

suggest_accuracy(addTo, modelTag = NULL, time = FALSE)

Arguments

addTo	Summary list that contains model fits to compare.
modelTag	Select model fits that contains modelTag in their name.
time	If TRUE, calculates average time to train model for a single tuning.

Examples

## Not run: 
library(mlbench)
data(PimaIndiansDiabetes)
index <- sample(seq_len(nrow(PimaIndiansDiabetes)), 500)
trainingSet <- PimaIndiansDiabetes[index, ]
testSet <- PimaIndiansDiabetes[-index, ]
x <- trainingSet[, -9]
y <- trainingSet[, 9]
x_test <- testSet[, -9]
y_test <- testSet[, 9]
sSummary <- list()
sSummary <- add_model(sSummary, x, y)
sSummary <- add_model(sSummary, x, y, model = c("C5.0Cost", "glmnet"), modelTag = "others")
 
suggest_accuracy(sSummary)
suggest_accuracy(sSummary, time = TRUE)
suggest_accuracy(sSummary, time = TRUE, modelTag = "glm|svm")

# vignette("modeval") #check a vignette for further details

## End(Not run)

modeval documentation built on May 29, 2017, 10:54 a.m.