skater (version 0.1.2)

confusion_matrix: Calculate various statistics from a confusion matrix

Description

Given a vector of predictions and target values, calculate numerous statistics of interest. Modified from m-clark/confusion_matrix.

Usage

confusion_matrix(
  prediction,
  target,
  positive = NULL,
  prevalence = NULL,
  dnn = c("Predicted", "Target"),
  longer = FALSE,
  ...
)

Value

A list of tibble(s) with the associated statistics and possibly the frequency table as list column of the first element. If classes contain >1 numeric class and a single non-numeric class (e.g., "1", "2", "3", and "Unrelated", the RMSE of the reciprocal of the Targets + 0.5 will also be returned.)

Arguments

prediction

A vector of predictions

target

A vector of target values

positive

The positive class for a 2-class setting. Default is NULL, which will result in using the first level of target.

prevalence

Prevalence rate. Default is NULL.

dnn

The row and column headers for the contingency table returned. Default is 'Predicted' for rows and 'Target' for columns.

longer

Transpose the output to long form. Default is FALSE (requires tidyr 1.0).

...

Other parameters, not currently used.

Details

This returns accuracy, agreement, and other statistics. See the functions below to find out more. Originally inspired by the confusionMatrix function from the caret package.

References

Kuhn, M., & Johnson, K. (2013). Applied predictive modeling.

See Also

calc_accuracy calc_stats

Examples

prediction = c(0,1,1,0,0,1,0,1,1,1)
target     = c(0,1,1,1,0,1,0,1,0,1)
confusion_matrix(prediction, target, positive = '1')

set.seed(42)
prediction = sample(letters[1:4], 250, replace = TRUE, prob = 1:4)
target     = sample(letters[1:4], 250, replace = TRUE, prob = 1:4)
confusion_matrix(prediction, target)

prediction = c(rep(1, 50), rep(2, 40), rep(3, 60))
target     = c(rep(1, 50), rep(2, 50), rep(3, 50))
confusion_matrix(prediction, target)
confusion_matrix(prediction, target) %>% purrr::pluck("Table")
confusion_matrix(prediction, target, longer=TRUE)
confusion_matrix(prediction, target, longer=TRUE) %>%
  purrr::pluck("Other") %>%
  tidyr::spread(Class, Value)

# Prediction with an unrelated class
prediction = c(rep(1, 50), rep(2, 40), rep(3, 60), rep("Unrelated", 55))
target     = c(rep(1, 50), rep(2, 50), rep(3, 55), rep("Unrelated", 50))
confusion_matrix(prediction, target)
# Prediction with two unrelated classes
prediction = c(rep(1, 50), rep(2, 40), rep("Third", 60), rep("Unrelated", 55))
target     = c(rep(1, 50), rep(2, 50), rep("Third", 55), rep("Unrelated", 50))
confusion_matrix(prediction, target)