Consider showing balanced accuracy instead of accuracy + additional accuracy metrics

Currently, Model builder shows accuracy for the user because it is intuitive to understand.
However, in cases on imbalanced datasets, accuracy can be misleading. I believe real-world datasets are rarely perfectly balanced and in this cases it is no longer intuitive to understand what accuracy means.

For examples, I might have 10% false and 90% true in my dataset. If Model would predict everything as true I still get 90% accuracy.

The issues may even be worse in near-balanced datasets, for example, 60/40% because user might not even think about this possibility.

Even if ML.NET does not have average / balanced accuracy score, it can be calculated from other metrics.

Alternatives for, or additions to, balanced accuracy:
- show the distribution of test data across classes.
- show confusion matrix
- show null accuracy

Esamina l'output di valutazione corrente di Model Builder e il modo in cui calcola e visualizza l'accuratezza, quindi confronta le metriche disponibili per i dataset sbilanciati. Decidi se balanced accuracy, la distribuzione delle classi, una matrice di confusione o null accuracy affronta al meglio il problema e verifica l'approccio scelto su esempi sbilanciati.

Scritto dal modello di indicizzazione a partire dal testo della issue.