METRICS

from sklearn import metrics

C="Cat"
F="Fish"
H="Hen"

y_true = [C,C,C,C,C,C, F,F,F,F,F,F,F,F,F,F, H,H,H,H,H,H,H,H,H]
y_pred = [C,C,C,C,H,F, C,C,C,C,C,C,H,H,F,F, C,C,C,H,H,H,H,H,H]

print(metrics.confusion_matrix(y_true, y_pred))

[[4 1 1]
 [6 2 2]
 [3 0 6]]

print(metrics.classification_report(y_true, y_pred, digits=3))

              precision    recall  f1-score   support

         Cat      0.308     0.667     0.421         6
        Fish      0.667     0.200     0.308        10
         Hen      0.667     0.667     0.667         9

    accuracy                          0.480        25
   macro avg      0.547     0.511     0.465        25
weighted avg      0.581     0.480     0.464        25