Package CONFUSION-MATRIX

Package name	CONFUSION-MATRIX
Nicknames	CM

Package documentation

Lisp library for creating a confusion matrix, incrementally adding information, and retrieving statistical information.

documentation	https://peterlane.codeberg.page/confusion-matrix-lisp/
repository	https://codeberg.org/peterlane/confusion-matrix-lisp/

COHEN-KAPPA

Lambda list: cohen-kappa ( cm &key (for-label (first (confusion-matrix-labels cm))) )
Documentation: Cohen’s Kappa statistic compares observed accuracy with an expected accuracy

CONFUSION-MATRIX-ADD

Lambda list: confusion-matrix-add ( cm predicted observed &optional (total 1) )
Documentation: Adds total to the count for given (predicted observed) labels, errors if labels not known

CONFUSION-MATRIX-COUNT

Lambda list: confusion-matrix-count ( cm predicted observed )
Documentation: Returns the count for given (predicted observed) labels, errors if labels not known

CONFUSION-MATRIX-P

CONFUSION-MATRIX-TOTAL

Lambda list: confusion-matrix-total ( cm )
Documentation: Returns the total number of instances referenced in the confusion matrix

F-MEASURE

Lambda list: f-measure ( cm &key (for-label (first (confusion-matrix-labels cm))) )
Documentation: Harmonic mean of the precision and recall for the given label

FALSE-NEGATIVE

Lambda list: false-negative ( cm &key (for-label (first (confusion-matrix-labels cm))) )
Documentation: Returns the number of instances of the given label which are incorrectly observed

FALSE-POSITIVE

Lambda list: false-positive ( cm &key (for-label (first (confusion-matrix-labels cm))) )
Documentation: Returns the number of instances incorrectly observed of the given label

FALSE-RATE

Lambda list: false-rate ( cm &key (for-label (first (confusion-matrix-labels cm))) )
Documentation: Proportion of instances of label incorrectly observed out of all instances not originally of that label

GEOMETRIC-MEAN

MAKE-CONFUSION-MATRIX

Lambda list: make-confusion-matrix ( &key (labels '(positive negative)) (counts (mapcar #'(lambda (pair) (cons pair 0)) (map-product 'list labels labels))) )
Documentation: NIL

MATTHEWS-CORRELATION

Lambda list: matthews-correlation ( cm &key (for-label (first (confusion-matrix-labels cm))) )
Documentation: Measure of the quality of binary classifications

OVERALL-ACCURACY

PRECISION

Lambda list: precision ( cm &key (for-label (first (confusion-matrix-labels cm))) )
Documentation: Proportion of instances of given label which are correct

PREVALENCE

Lambda list: prevalence ( cm &key (for-label (first (confusion-matrix-labels cm))) )
Documentation: Proportion of instances observed of given label, out of total

RECALL

Lambda list: recall ( cm &key (for-label (first (confusion-matrix-labels cm))) )
Documentation: Recall is equal to the true rate, for a given label

SENSITIVITY

Lambda list: sensitivity ( cm &key (for-label (first (confusion-matrix-labels cm))) )
Documentation: Sensitivity is another name for the true positve rate (recall), for a given label

SPECIFICITY

Lambda list: specificity ( cm &key (for-label (first (confusion-matrix-labels cm))) )
Documentation: Specificity is 1 - false-rate, for a given label

TRUE-NEGATIVE

Lambda list: true-negative ( cm &key (for-label (first (confusion-matrix-labels cm))) )
Documentation: Returns the number of instances NOT of the given label which are correctly observed

TRUE-POSITIVE

Lambda list: true-positive ( cm &key (for-label (first (confusion-matrix-labels cm))) )
Documentation: Returns the number of instances of the given label correctly observed

TRUE-RATE

Lambda list: true-rate ( cm &key (for-label (first (confusion-matrix-labels cm))) )
Documentation: Proportion of instances of given label which are correctly observed