class_balance_accuracy_score_r: Class Balance Accuracy Score
In nhs-r-community/pxtextmineR: An R Wrapper for Python's "pxtextmining" library

Description Usage Arguments Details Value References Examples

View source: R/class_balance_accuracy_score_r.R

Assess classifier performance with Mosley's (2013) Class Balance Accuracy score.

1	class_balance_accuracy_score_r(y_true, y_pred)

`y_true`	Vector. The true classes.
`y_pred`	Vector. The predicted classes.

The Class Balance Accuracy Score is a type of balanced accuracy score to assess classifier performance on data with imbalanced classes in multi-class supervised learning.

From Mosley (2013, p. 41–42): "From its construction, CBA utilizes three core elements from each class within the contingency table: the total number of correctly classified cases, the total number of cases predicted into that class, and the total number observed in the data. Intuitively, for each class the off-diagonal row and column elements are reduced into a single sum. These singular sums form the basis for the denominator of the per class accuracy contributions. At the bottom of each per class ratio, the maximum of the row or column sum is chosen resulting in either the Recall or Precision to be the estimate of class accuracy. As a consequence, selecting the larger of the two as the denominator provides the most conservative estimate of accuracy that can be achieved. For each class, the per class Recall or Precision are aggregated and treated as the numerator for the final ratio calculation. By using the total number of classes in the dataset as the divisor in the calculation we guarantee equal weight contributions for all classes. In the end, Class Balance Accuracy acts as a measure that independently accounts for the ability of the model to precisely recall observations from each group within the target variable."

Numeric. The Class Balance Accuracy score.

Mosley L. (2013). A balanced approach to the multi-class imbalance problem. Graduate Theses and Dissertations. 13537. https://lib.dr.iastate.edu/etd/13537.

x <- pxtextmineR::text_data %>%
  dplyr::mutate(label_pred = sample(label, size = nrow(.))) # Mock predictions column

pxtextmineR::class_balance_accuracy_score_r(x$label, x$label_pred)