혼동 행렬과 오류 분석

A confusion matrix lays out predicted versus true labels in a grid. One glance shows exactly where your model trips up.

Rows are the true classes and columns are the predicted ones. The diagonal holds correct calls; everything off it is an error.

Scikit-learn turns labels and predictions into the grid instantly, so you can inspect the counts right away.

A strong model concentrates counts on the diagonal. Large off-diagonal cells reveal pairs of classes it keeps confusing.

If many cats get labeled dogs, that single bright off-diagonal cell points you straight to a systematic weakness.

Raw counts mislead when classes differ in size. Normalize each row to compare error rates per class on equal footing.

Numbers tell you where, not why. Error analysis means opening the actual misclassified samples to understand the cause.

Sort errors into buckets like blurry images or rare phrasing. Patterns in these groups suggest concrete fixes.

Some misses are really wrong labels in your data, not model faults. Error analysis surfaces these so you can clean them.

Each error pattern maps to a remedy: more data, better features, or a tweaked threshold for the troublesome class.

Repeat error analysis after every change. This steady loop turns vague failure into a clear, prioritized to-do list. 🔁

A confusion matrix shows where errors fall, then hands-on error analysis explains why. Together they turn failures into fixes. 🎯