【1】評價分類模型，我們一般從兩個點出發

1.通過指標來評估該模型是否適合對預測對象？?評價指標主要有：1）Precision；2）Recall；3）F-score;4)Accuracy;5)ROC;6)AUC[1]?

2.通過計算預測模型所產生的模擬值與歷史實際值擬合程度的優劣來估計該模型的預測值的擬合效果。

指標有：預測精度和預測準確度，兩者是不同的概念。 [2]

準確率accuracy 和 精度precision 的不同： [3]

（1）Accuracy is how close a measured value is to the actual (true) value.

準確率是測量值與實際（真）值的接近程度。

（2）Precision is how close the measured values are to each other.

精度是測量值彼此接近的程度。 ????

Examples of Precision and Accuracy:

Low Accuracy High Precision	High Accuracy Low Precision	High Accuracy High Precision

So, if you are playing soccer and you always hit the left goal post instead of scoring, then you are?not?accurate, but you?are?precise!

對于 預測精度和預測準確度的區分，你也可以參見 wikipedia：[4] ? Accuracy and precision

Accuracy is the proximity of measurement results to the true value; precision, the repeatability, or reproducibility of the measurement. ?(準確度是測量結果與真實值的接近程度，而精度表示重復性，測量值是否集中)

According to ISO 5725-1,^[5]?the general term "accuracy" is used to describe the closeness of a measurement to the true value. When the term is applied to sets of measurements of the same?measurand, it involves a component of random error and a component of systematic error. In this case?trueness?is the closeness of the mean of a set of measurement results to the actual (true) value and?precision?is the closeness of agreement among a set of results.

References

[1]?分類算法中常用的評價指標

[2]?預測精度

[3]?Accuracy and Precision(www.mathsisfun.com)

[4]?Accuracy and Precision( wikipedia)

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