机器学习 - metric评估方法

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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