【1】评价分类模型，我们一般从两个点出发

1.通过指标来评估该模型是否适合对预测对象？ 评价指标主要有：1）Precision；2）Recall；3）F-score;4)Accuracy;5)ROC;6)AUC[1] 

2.通过计算预测模型所产生的模拟值与历史实际值拟合程度的优劣来估计该模型的预测值的拟合效果。

指标有：预测精度和预测准确度，两者是不同的概念。 [2]

准确率accuracy 和 精度precision 的不同： [3]

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

准确率是测量值与实际（真）值的接近程度。

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

精度是测量值彼此接近的程度。 ​​​​

Examples of Precision and Accuracy:

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!

对于 预测精度和预测准确度的区分，你也可以参见 wikipedia：[4]   Accuracy and precision

Accuracy is the proximity of measurement results to the true value; precision, the repeatability, or reproducibility of the measurement.  (准确度是测量结果与真实值的接近程度，而精度表示重复性，测量值是否集中)

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] 分类算法中常用的评价指标

[2] 预测精度

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

[4] Accuracy and Precision( wikipedia)