有一些方法来评估classification model。
| Metric name / Evaluation method | Definition | Code |
|---|---|---|
| Accuracy | Out of 100 predictions, how many does your model get correct? E.g. 95% accuracy means it gets 95/100 predictions correct. | torchmetrics.Accuracy() or sklearn.metrics.accuracy_score() |
| Precision | Proportion of true positive over total number of samples. Higher precision leads to less false positives (model predicts 1 when it should’ve been 0). | torchmetrics.Precision() or sklearn.metrics.precision_score() |
| Recall | Proportion of true positives over total number of true positives and false negatives (model predicts 0 when it should’ve been 1). Higher recall leads to less false negatives. | torchmetrics.Recall() or sklearn.metrics.recall_score() |
| F1-score | Combines precision and recall into one metric, 1 is best, 0 is worst | torchmetrics.F1Score() or sklearn.metrics.f1_score() |
| Confusion matrix | Compares the predicted values with the true values in a tabular way, if 100% correct, all values in the matrix will be top left to bottom right (diagnoal line). | torchmetrics.ConfusionMatrix or sklearn.metrics.plot_confusion_matrix() |
| Classification report | Collection of some of the main classification metrics such as precision, recall and f1-score. | sklearn.metrics.classification_report() |
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