eval.classification.results: Determine the performance of classification

Description Usage Arguments Details Value See Also Examples

View source: R/classification.R

Description

The Momocs::classification_metrics() function as well as the mltools::mcc() fucntion are used to generate metrics to evaluate the performance of classification.

Usage

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eval.classification.results(actual, predicted, name = "")

Arguments

actual

A character vector that indicates the actual class for each observation.

predicted

A character vector that indicates the predicted class for each observation.

name

A string that specifies a name to assign to this iteration of the function. Default is blank.

Details

Helpful for reference: https://rdrr.io/cran/Momocs/man/classification_metrics.html

https://blog.revolutionanalytics.com/2016/03/com_class_eval_metrics_r.html

https://github.com/saidbleik/Evaluation

Value

A list with 4 objects:

  1. Name. Helpful if this function is put into a loop and the output is meant to be written into a separate text file. Otherwise, just leave blank.

  2. Contingency table used for calculating classification metrics by Momocs.

  3. Metrics calculated by Momocs.

  4. MCC value calculated by mltools labeled.

See Also

Other Classification functions: CVPredictionsRandomForest(), CVRandomForestClassificationMatrixForPheatmap(), GenerateExampleDataMachinelearnr(), LOOCVPredictionsRandomForestAutomaticMtryAndNtree(), LOOCVRandomForestClassificationMatrixForPheatmap(), RandomForestAutomaticMtryAndNtree(), RandomForestClassificationGiniMatrixForPheatmap(), RandomForestClassificationPercentileMatrixForPheatmap(), find.best.number.of.trees()

Examples

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id = c("1a", "1b", "1c", "1d", "1e", "1f", "1g", "2a", "2b", "2c", "2d", "2e", "2f", "3a",
       "3b", "3c", "3d", "3e", "3f", "3g", "3h", "3i")

x = c(18, 21, 22, 24, 26, 26, 27, 30, 31, 35, 39, 35, 30, 40, 41, 42, 44, 46, 47, 48, 49, 54)

y = c(10, 11, 22, 15, 12, 13, 14, 33, 39, 37, 44, 40, 45, 27, 29, 20, 28, 21, 30, 31, 23, 24)

a = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)

b = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1)


actual = as.factor(c("1", "1", "1", "1", "1", "1", "1", "2", "2", "2", "2", "2", "2", "3", "3", "3",
       "3", "3", "3", "3", "3", "3"))

example.data <- data.frame(id, x, y, a, b, actual)

set.seed(2)
rf.result <- randomForest::randomForest(x=example.data[,c("x", "y", "a", "b")],
y=example.data[,"actual"], proximity=TRUE)

predicted <- rf.result$predicted
actual <- example.data[,"actual"]

results <- eval.classification.results(as.character(actual), as.character(predicted), "Example")

yhhc2/machinelearnr documentation built on Dec. 23, 2021, 7:19 p.m.