Evaluation metrics
Lighthouse automatically generates a suite of evaluation metrics. Here, we briefly describe these. This page uses terms defined in Terminology, so see that page for any unfamiliar words.
Confusion matrices
Lighthouse plots confusion matrices, which are simple tables showing the empirical distribution of predicted class (the rows) versus the elected class (the columns). These come in two variants:
- row-normalized: this means each row has been normalized to sum to 1. Thus, the row-normalized confusion matrix shows the empirical distribution of elected classes for a given predicted class. E.g. the first row of the row-normalized confusion matrix shows the empirical probabilities of the elected classes for a sample which was predicted to be in the first class.
- column-normalized: this means each column has been normalized to sum to 1. Thus, the column-normalized confusion matrix shows the empirical distribution of predicted classes for a given elected class. E.g. the first column of the column-normalized confusion matrix shows the empirical probabilities of the predicted classes for a sample which was elected to be in the first class.
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