判斷結果好不好的方法
分類指標(目標為離散的)
參考網站
Accuracy
- 所有分類正確的樣本佔全部樣本的比例
- ACC = (tp + tn) / 全部樣本
Precision & Recall
參考網站
評估模型指標
[例子]
- 用"狼來了"的故事,分成四類
- 前情 : positive 檢測到狼.negative 沒檢測到狼.
- 這裡的狼代表真實結果,呼喊代表預測結果
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- True Positives : 狼來,小孩叫
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- False Positives : 狼沒來,小孩叫
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- False Negatives : 狼來,小孩沒叫
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- True Negatives : 狼沒來,小孩也沒叫
Precision : 當小孩說了狼來了,真的狼來了機率
- True Positive Rate
- 這個值越大越好
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Recall : 進入村莊的狼,我們發現的機率
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這個值越大越好
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兩個機率相互影響,如果Recall 要提升,連風吹草動都要說狼來了
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如果不想要說了之後發現狼沒來,那就要變嚴格
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有一個指標是ROC 指標
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結合上面兩者得出的
f1 score
f2 score
ROC
參考網址
- 不同 thershold 的recall & precision 結果曲線
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AUC(曲線下面積) = 表示“ROC 曲線下面積”
- 用AUC 的計算 : AUC值越大,結果越正確,如果AUC = 0.5 ,那就和隨機丟銅板一樣
- 我們希望AUC = 1(但是是不可能的)
F - Score
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- f 後面的數字是 β 的值
- β = 1 => f1
迴歸指標(目標為連續的數值)
RMSE
MSE
MAE