# Metrics

Compute metrics for assessing the performance of classification/regression models.

## Classification

Examples:

    >>> import mlpy
    >>> t = [3,2,3,3,3,1,1,1]
    >>> p = [3,2,1,3,3,2,1,1]
    >>> mlpy.error(t, p)
    0.25
    >>> mlpy.accuracy(t, p)
    0.75

### Binary Classification Only

The Confusion Matrix:

Total Samples (ts) | Actual Positives (ap) | Actual Negatives (an)
Predicted Positives (pp) | True Positives (tp) | False Positives (fp)
Predicted Negatives (pn) | False Negatives (fn) | True Negatives (tn)

Examples:

    >>> import mlpy
    >>> t = [1, 1, 1,-1, 1,-1,-1,-1]
    >>> p = [1,-1, 1, 1, 1,-1, 1,-1]
    >>> mlpy.error_p(t, p)
    0.25
    >>> mlpy.error_n(t, p)
    0.5
    >>> mlpy.sensitivity(t, p)
    0.75
    >>> mlpy.specificity(t, p)
    0.5
    >>> mlpy.ppv(t, p)
    0.59999999999999998
    >>> mlpy.npv(t, p)
    0.66666666666666663
    >>> mlpy.mcc(t, p)
    0.2581988897471611
    >>> p = [2.3,-0.4, 1.6, 0.6, 3.2,-4.9, 1.3,-0.3]
    >>> mlpy.auc_wmw(t, p)
    0.8125
    >>> p = [2.3,0.4, 1.6, -0.6, 3.2,-4.9, -1.3,-0.3]
    >>> mlpy.auc_wmw(t, p)
    1.0

## Regression

Example:

    >>> import mlpy
    >>> t = [2.4,0.4,1.2,-0.2,3.3,-4.9,-1.1,-0.1]
    >>> p = [2.3,0.4,1.6,-0.6,3.2,-4.9,-1.3,-0.3]
    >>> mlpy.mse(t, p)
    0.052499999999999998