问题的答案phi，Matthews和Pearson相关系数之间的关系？表明三种系数方法都是等效的。
我不是来自统计数据，所以这应该是一个简单的问题。
Matthews的论文（www.sciencedirect.com/science/article/pii/0005279575901099）描述了以下内容：
"A correlation of:
C = 1 indicates perfect agreement,
C = 0 is expected for a prediction no better than random, and
C = -1 indicates total disagreement between prediction and observation"`.
根据Wikipedia（http://en.wikipedia.org/wiki/Pearson_product-moment_correlation_coefficient），皮尔森相关性描述为：
giving a value between +1 and −1 inclusive, where:
1 is total positive correlation,
0 is no correlation, and
−1 is total negative correlation
最好理解Pearson相关系数的解释如下（根据http://faculty.quinnipiac.edu/libarts/polsci/Statistics.html）：
If r =
+.70 or higher Very strong positive relationship
+.40 to +.69 Strong positive relationship
+.30 to +.39 Moderate positive relationship
+.20 to +.29 weak positive relationship
+.01 to +.19 No or negligible relationship
-.01 to -.19 No or negligible relationship
-.20 to -.29 weak negative relationship
-.30 to -.39 Moderate negative relationship
-.40 to -.69 Strong negative relationship
-.70 or higher Very strong negative relationship
阅读一些论文，对MCC结果范围在-1和1之间没有任何程度的解释。此系数对于负数和正数的不平衡数据集非常有用，如果在这种情况下预测变量是准确的，则精度度量无法很好地估计。
对于不平衡的数据集，F度量是与MCC进行比较以评估预测器性能的好指标吗？例如：在某些情况下，F-measure = 94%和MCC = 0.58。它对预测变量有什么启示？
对于Matthews相关系数，我可以采用相同的解释吗，或者在解释上有不同的含义？我相信这两个系数在解释中也相等。