Accuracy

accuracy.factor

R Documentation

Description

A generic function for the (normalized) accuracy in classification tasks. Use weighted.accuracy() for the weighted accuracy.

Usage

## S3 method for class 'factor'
accuracy(actual, predicted, ...)

## S3 method for class 'factor'
weighted.accuracy(actual, predicted, w, ...)

## S3 method for class 'cmatrix'
accuracy(x, ...)

## Generic S3 method
accuracy(...)

## Generic S3 method
weighted.accuracy(
...,
w
)

Arguments

actual	A vector of with length \(n\), and \(k\) levels
predicted	A vector of with length \(n\), and \(k\) levels
…	micro = NULL, na.rm = TRUE Arguments passed into other methods
w	A <numeric>-vector of length \(n\). NULL by default
x	A confusion matrix created cmatrix()

Value

A <numeric>-vector of length 1

Definition

Let \(\hat{\alpha} \in [0, 1]\) be the proportion of correctly predicted classes. The accuracy of the classifier is calculated as,

\[ \hat{\alpha} = \frac{\#TP + \#TN}{\#TP + \#TN + \#FP + \#FN} \]

Where:

• \(\#TP\) is the number of true positives,

• \(\#TN\) is the number of true negatives,

• \(\#FP\) is the number of false positives, and

• \(\#FN\) is the number of false negatives.

Examples

# 1) recode Iris
# to binary classification
# problem
iris$species_num <- as.numeric(
  iris$Species == "virginica"
)

# 2) fit the logistic
# regression
model <- glm(
  formula = species_num ~ Sepal.Length + Sepal.Width,
  data    = iris,
  family  = binomial(
    link = "logit"
  )
)

# 3) generate predicted
# classes
predicted <- factor(
  as.numeric(
    predict(model, type = "response") > 0.5
  ),
  levels = c(1,0),
  labels = c("Virginica", "Others")
)

# 3.1) generate actual
# classes
actual <- factor(
  x = iris$species_num,
  levels = c(1,0),
  labels = c("Virginica", "Others")
)

# 4) evaluate model
# performance
cat(
  "Accuracy", accuracy(
    actual    = actual,
    predicted = predicted
  ),

  "Accuracy (weigthed)", weighted.accuracy(
    actual    = actual,
    predicted = predicted,
    w         = iris$Petal.Length/mean(iris$Petal.Length)
  ),
  sep = "\n"
)