Binary classification measure defined as $$ \frac{\mathrm{TP} \cdot \mathrm{TN} - \mathrm{FP} \cdot \mathrm{FN}}{\sqrt{(\mathrm{TP} + \mathrm{FP}) (\mathrm{TP} + \mathrm{FN}) (\mathrm{TN} + \mathrm{FP}) (\mathrm{TN} + \mathrm{FN})}}. $$

mcc(truth, response, positive, ...)

truth | :: |
---|---|

response | :: |

positive | :: |

... | :: |

Performance value as `numeric(1)`

.

This above formula is undefined if any of the four sums in the denominator is 0. The denominator is then set to 1.

Type:

`"binary"`

Range: \([-1, 1]\)

Minimize:

`FALSE`

Required prediction:

`response`

Matthews BW (1975).
“Comparison of the predicted and observed secondary structure of T4 phage lysozyme.”
*Biochimica et Biophysica Acta (BBA) - Protein Structure*, **405**(2), 442--451.
doi: 10.1016/0005-2795(75)90109-9
.

Other Binary Classification Measures:
`auc()`

,
`bbrier()`

,
`dor()`

,
`fbeta()`

,
`fdr()`

,
`fnr()`

,
`fn()`

,
`fomr()`

,
`fpr()`

,
`fp()`

,
`npv()`

,
`ppv()`

,
`tnr()`

,
`tn()`

,
`tpr()`

,
`tp()`

set.seed(1) lvls = c("a", "b") truth = factor(sample(lvls, 10, replace = TRUE), levels = lvls) response = factor(sample(lvls, 10, replace = TRUE), levels = lvls) mcc(truth, response, positive = "a")#> [1] -0.25