Merge pull request microsoft#235 from RyanXinOne/ms-main

Correct the definitions of precision and recall in 2-4-Logistic.
creadicted · Jul 29, 2021 · cacef4e · cacef4e
2 parents d6e61a3 + 01109e7
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diff --git a/2-Regression/4-Logistic/README.md b/2-Regression/4-Logistic/README.md
@@ -237,9 +237,9 @@ As you might have guessed it's preferable to have a larger number of true positi
 
 Let's revisit the terms we saw earlier with the help of the confusion matrix's mapping of TP/TN and FP/FN:
 
-🎓 Precision: TP/(TP + FN) The fraction of relevant instances among the retrieved instances (e.g. which labels were well-labeled)
+🎓 Precision: TP/(TP + FP) The fraction of relevant instances among the retrieved instances (e.g. which labels were well-labeled)
 
-🎓 Recall: TP/(TP + FP) The fraction of relevant instances that were retrieved, whether well-labeled or not
+🎓 Recall: TP/(TP + FN) The fraction of relevant instances that were retrieved, whether well-labeled or not
 
 🎓 f1-score: (2 * precision * recall)/(precision + recall) A weighted average of the precision and recall, with best being 1 and worst being 0
 

diff --git a/2-Regression/4-Logistic/translations/README.id.md b/2-Regression/4-Logistic/translations/README.id.md
@@ -245,9 +245,9 @@ Mari kita lihat kembali istilah-istilah yang kita lihat tadi dengan bantuan matr
 > NB: Negatif benar
 > NP: Negatif palsu
 
-🎓 Presisi: PB/(PB + NP) Rasio titik data relevan antara semua titik data (seperti data mana yang benar dilabelkannya)
+🎓 Presisi: PB/(PB + PP) Rasio titik data relevan antara semua titik data (seperti data mana yang benar dilabelkannya)
 
-🎓 *Recall*: PB/(PB + PP) Rasio titk data relevan yang digunakan, maupun labelnya benar atau tidak.
+🎓 *Recall*: PB/(PB + NP) Rasio titk data relevan yang digunakan, maupun labelnya benar atau tidak.
 
 🎓 *f1-score*: (2 * Presisi * *Recall*)/(Presisi + *Recall*) Sebuah rata-rata tertimbang antara presisi dan *recall*. 1 itu baik dan 0 itu buruk.
 

diff --git a/2-Regression/4-Logistic/translations/README.it.md b/2-Regression/4-Logistic/translations/README.it.md
@@ -238,9 +238,9 @@ Come si sarà intuito, è preferibile avere un numero maggiore di veri positivi
 
 I termini visti in precedenza vengono rivisitati con l'aiuto della mappatura della matrice di confusione di TP/TN e FP/FN:
 
-🎓 Precisione: TP/(TP + FN) La frazione di istanze rilevanti tra le istanze recuperate (ad es. quali etichette erano ben etichettate)
+🎓 Precisione: TP/(TP + FP) La frazione di istanze rilevanti tra le istanze recuperate (ad es. quali etichette erano ben etichettate)
 
-🎓 Richiamo: TP/(TP + FP) La frazione di istanze rilevanti che sono state recuperate, ben etichettate o meno
+🎓 Richiamo: TP/(TP + FN) La frazione di istanze rilevanti che sono state recuperate, ben etichettate o meno
 
 🎓 f1-score: (2 * precisione * richiamo)/(precisione + richiamo) Una media ponderata della precisione e del richiamo, dove il migliore è 1 e il peggiore è 0
 

diff --git a/2-Regression/4-Logistic/translations/README.zh-cn.md b/2-Regression/4-Logistic/translations/README.zh-cn.md
@@ -238,9 +238,9 @@ Seaborn提供了一些巧妙的方法来可视化你的数据。例如，你可
 
 让我们借助混淆矩阵对TP/TN和FP/FN的映射，重新审视一下我们之前看到的术语：
 
-🎓 准确率：TP/（TP+FN）检索实例中相关实例的分数（例如，哪些标签标记得很好）
+🎓 准确率：TP/(TP + FP) 检索实例中相关实例的分数（例如，哪些标签标记得很好）
 
-🎓 召回率: TP/(TP + FP) 检索到的相关实例的比例，无论是否标记良好
+🎓 召回率: TP/(TP + FN) 检索到的相关实例的比例，无论是否标记良好
 
 🎓 F1分数: (2 * 准确率 * 召回率)/(准确率 + 召回率) 准确率和召回率的加权平均值，最好为1，最差为0