準確率，精準率和召回率（Accuracy、precision & Recall）

最重要分清四個變量：
https://en.wikipedia.org/wiki/Sensitivity_and_specificity

1. True positive (TP): e.g., Sick people correctly identified as sick
2. False positive (FP): e.g., Healthy people incorrectly identified as sick
3. True negative (TN): e.g., Healthy people correctly identified as healthy
4. False negative (FN): e.g., Sick people incorrectly identified as healthy

In general, Positive = identified and negative = rejected. Therefore:

1. True positive = correctly identified
2. False positive = incorrectly identified
3. True negative = correctly rejected
4. False negative = incorrectly rejected

所以：
condition positive （P)：the number of real positive cases in the data
condition negative (N)：the number of real negative cases in the data

即 P = TP + FN, F = TN + FP

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準確率 Accuracy = (TP + TN) / (P + N) = (TP + TN) / (TP + FN + TN + FP)

精確率Precision = TP / (TP + FP)

召回率Recall = TP / P = TP / (TP + FN)

F1-Score = 2 * (Precision * Recall) / (Precision + Recall) = 2 * TP / (2 * TP + FP +FN)

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示例說明：
1000個病人，現被確診400個病人，600個健康人。
這400個病人實際上有300個病人（TP）和100給誤判的健康人（FP）；
而這判定爲健康的600人中，有500個是健康人（TN），100個病人（FN）。
即: TP = 300， FP = 100， TN = 500， FN = 100.

注：實際上的患者爲500人（P = TP + FN），500個健康人（F = TN + FP）

所以:
準確率Accuray = (TP + TN) / (TP + FN + TN + FP) = 800 / 1000 = 80%
精確率Precision = TP / (TP + FP) = 300 / （300 + 100）= 75%
召回率Recall = TP / (TP + FN) = 300 / （300 + 100） = 75%
F1-score = 2 * (Precision * Recall) / (Precision + Recall) = 75%