confusion_matrix: Confusion Matrix
In chuvanan/metrics: Metrics For Evaluating Machine Learning Models

Description Usage Arguments Value Author(s) Examples

Confusion matrix - a special kind of contingency table - is a specific table layout that allows the visualization of performance of a classification model (or classifier). It composes of four different combinations of predicted and actual values.

confusion_list computes and returns those combinations as a named list while mtr_confusion_matrix returns a named matrix.

Here's elements of confusion matrix:

True Positive (TP)
False Positive (FP)
True Negative (TN)
False Negative (FN)

1
2
3

confusion_list(actual, predicted, cutoff = 0.5)

mtr_confusion_matrix(actual, predicted, cutoff = 0.5)

`actual`	`[numeric]` Ground truth binary numeric vector containing 1 for the positive class and 0 for the negative class.
`predicted`	`[numeric]` A vector of estimated probabilities.
`cutoff`	`[numeric]` A cutoff value for `predicted` vector which classify a sample into a given class. Default value is 0.5

A named list or a named two dimensions matrix

An Chu

act <- c(1, 0, 0, 1, 1)
pred <- c(0.9, 0.3, 0.6, 0.5, 0.2)

## output as a R's list
## metrics:::confusion_list(act, pred) # default value of cutoff = 0.5
## metrics:::confusion_list(act, pred, cutoff = 0.7)

## output as a R's matrix
mtr_confusion_matrix(act, pred)
mtr_confusion_matrix(act, pred, cutoff = 0.7)