scikit-learn Classification Analyzing Classification Reports


Example

Build a text report showing the main classification metrics, including the precision and recall, f1-score (the harmonic mean of precision and recall) and support (the number of observations of that class in the training set).

Example from sklearn docs:

from sklearn.metrics import classification_report
y_true = [0, 1, 2, 2, 2]
y_pred = [0, 0, 2, 2, 1]
target_names = ['class 0', 'class 1', 'class 2']
print(classification_report(y_true, y_pred, target_names=target_names))

Output -

         precision    recall  f1-score   support

class 0       0.50      1.00      0.67         1
class 1       0.00      0.00      0.00         1
class 2       1.00      0.67      0.80         3

avg / total   0.70      0.60      0.61         5