Confusion Matrix

For storing results from a classification experiment and providing statistical information.

A confusion matrix is used in data-mining as a summary of the performance of a classification algorithm. Each row represents the actual class of an instance, and each column represents the predicted class of that instance, i.e. the class that they were classified as. Numbers at each (row, column) reflect the total number of instances of actual class "row" which were predicted to fall in class "column".

A two-class example is:

    Predicted       Predicted     | 
    Positive        Negative      | Actual
    ------------------------------+------------
        a               b         | Positive
        c               d         | Negative

Here, the value:

• a is the number of true positives (those labelled positive and classified positive)
• b is the number of false negatives (those labelled positive but classified negative)
• c is the number of false positives (those labelled negative but classified positive)
• d is the number of true negatives (those labelled negative and classified negative)

From this table we can calculate statistics like:

• true positive rate = a/(a+b)
• positive recall = a/(a+c)

Features:

• incrementally add results to build up a confusion matrix
• calculate any of a range of statistics from the matrix at any time
• output the matrix to text as a simple table

Example

The following example shows how to create a confusion matrix, add some results, and then print some statistics and the table itself.

use confusion_matrix;

fn main() {
    let mut cm = confusion_matrix::new();

    cm[("pos", "pos")] = 10;
    cm[("pos", "neg")] = 3;
    cm[("neg", "neg")] = 20;
    cm[("neg", "pos")] = 5;
    
    println!("Precision: {}", cm.precision("pos"));
    println!("Recall: {}", cm.recall("pos"));
    println!("MCC: {}", cm.matthews_correlation("pos"));
    println!("");
    println!("{}", cm);
}

Output:

Precision: 0.7692307692307693
Recall: 0.6666666666666666
MCC: 0.5524850114241865

Predicted |
neg pos   | Actual
----------+-------
 20   3   | neg
  5  10   | pos

MIT Licence

Copyright (c) 2021-23, Peter Lane peterlane@gmx.com

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