I have below an example I pulled from sklearn 's sklearn.metrics.classification_report documentation.
What I don't understand is why there are f1-score, precision and recall values for each class where I believe class is the predictor label? I thought the f1 score tells you the overall accuracy of the model. Also, what does the support column tell us? I couldn't find any info on that.
print(classification_report(y_true, y_pred, target_names=target_names)) 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