Lodestar.Metrics 0.2.0. This page is frozen at that release. Read the current documentation for what main says now. A link to a decision or a migration page follows main, and leaves the archive.

Normalization

Which sum ConfusionMatrix.ToArray divides each cell by.

public enum Normalization { None, True, Pred, All }

Members — None leaves the raw counts, or weights when the matrix is weighted. True divides each row by its own sum, so the diagonal reads as per-class recall. Pred divides each column by its own sum, so the diagonal reads as per-class precision. All divides every cell by the grand total, turning each into a share of the dataset.

Example — one matrix, three readings of the same cell.

using Lodestar.Metrics;

int[] yTrue = [1, 1, 1, 1, 0, 0, 0, 0];
int[] yPred = [1, 1, 0, 0, 1, 0, 0, 0];

ConfusionMatrix cm = ConfusionMatrix.Compute(yTrue, yPred);
double count = cm.ToArray(Normalization.None)[1, 1];      // => 2
double recall = cm.ToArray(Normalization.True)[1, 1];     // => 0.5
double precision = cm.ToArray(Normalization.Pred)[1, 1];  // => 0.6666…

Remarks — True is the one to reach for when drawing a heat map, because a row that sums to 1 lets a rare class and a common class be compared by eye; raw counts make every rare class look black. Pred answers the mirror question — "when the model says this, how often is it right" — and All is for reporting shares of a dataset.

The trap is that this is a projection and not a parameter on Compute. There is no such thing as a normalized ConfusionMatrix here, and that is deliberate: Accuracy, Precision and the rest read a matrix's cells directly, and would be silently wrong if those cells had become fractions — decision 0020.

A row, column or total that counted nothing divides to zero, not NaN, matching scikit-learn's nan_to_num.

Applies to — net10.0, netstandard2.0.

See also — ConfusionMatrix.ToArray, ConfusionMatrix.Compute, decision 0020, the Python equivalence table.

Members

Member	What it does