Prediction results

Prediction results refer to TP, FN, FP and TN values. They can be calculated using prediction_results:

using ClassificationMetrics

actual    = [0, 0, 0, 1, 1, 1, 2, 2, 2]
predicted = [1, 2, 0, 0, 0, 1, 0, 2, 0]

prediction_results(predicted, actual, sort_labels=true)

┌───┬────┬────┬────┬────┐
│   │ TP │ FN │ FP │ TN │
├───┼────┼────┼────┼────┤
│ 0 │  1 │  2 │  4 │  2 │
├───┼────┼────┼────┼────┤
│ 1 │  1 │  2 │  1 │  5 │
├───┼────┼────┼────┼────┤
│ 2 │  1 │  2 │  1 │  5 │
└───┴────┴────┴────┴────┘

It is possible to provide full label set in case not all classes are present in the data. Optional argument sort_labels specifies whether the labels should be sorted or not. The results are stored in a convenience struct PredictionResults that holds TP, FN, FP and TN values and the labels. It can be passed to classification_report to get further results or "pretty printed" in the terminal.

It is also possible to index the results and concatenate them with vcat:

using ClassificationMetrics

actual    = [0, 0, 0, 1, 1, 1, 2, 2, 2, 3, 3, 3]
predicted = [1, 2, 0, 0, 0, 1, 0, 2, 0, 2, 0, 3]

res = prediction_results(predicted, actual, sort_labels=true)
res1 = res[1:2]
res2 = res[4]

vcat(res1, res2)

┌───┬────┬────┬────┬────┐
│   │ TP │ FN │ FP │ TN │
├───┼────┼────┼────┼────┤
│ 0 │  1 │  2 │  5 │  4 │
├───┼────┼────┼────┼────┤
│ 1 │  1 │  2 │  1 │  8 │
├───┼────┼────┼────┼────┤
│ 3 │  1 │  2 │  0 │  9 │
└───┴────┴────┴────┴────┘

The indexing uses the order of the labels stored in PredictionResults.