confuse

Binary and Multi-class evaluation metrics for classification tasks in machine learning.

Supported metrics

Counts

• True positives
• False positives
• False negatives
• True negatives

Binary class Metrics

• sensitivity / specificity / true positive rate
• specificity / true negative rate
• false positive rate
• false negative rate
• precision
• f1-score
• accuracy
• misclassification-rate
• confusion matrix

Multi-class metrics

• Micro averaged fmeasure/precision/recall
• Macro averaged fmeasure/precision/recall

Usage

• Define the predicted and actuals in 2 different vectors
• Call any of the apis with the actuals, predicted vectors as arguments
• Some APIs require the positive class to be provided as a third argument

(:require [confuse.binary-class-metrics :refer :all])
(def pred [1 0 1 1 0])
(def ac [1 1 1 1 0])

;;assume the positive class is 1
(true-positives ac pred 1)
;;3

;;calculate accuracy
(accuracy ac pred)
;;0.8

;;predicted and actuals could be keywords too
(def spam-pred [:spam :spam :notspam :notspam :spam])
(def spam-actual [:spam :notspam :spam :notspam :spam])

;;pass the positive class as the third argument
(true-positives spam-actual spam-pred :spam)
;;2

;;Confusion matrix
;;returns a map where the key is frequency [predicted actual] and value is the count 
;;against that key 
(confusion-matrix spam-actual spam-pred)
;;{[:spam :spam] 2, [:spam :notspam] 1, [:notspam :spam] 1, [:notspam :notspam] 1}

License

Copyright © 2017 Datacraft Sciences

Distributed under the Eclipse Public License either version 1.0 or (at your option) any later version.