Re: CCL:Correlation Coefficient

 Hi Holly,
I would say there is no general answer and values of 0.8 or whatever seems suspect to me. Why? The quality depends highly on the data set used, even on data set with drug molecules [1]. Furthermore, if you apply an optimization algorithm to select a better set of features/descriptors, better induction parameters, etc. you can easily produce an overfitted result [2] (see references therein). So, there is no short answer, but yes you can use the cross-validation result (of the outer loop) to estimate the future performance of your hypothesis. In other words, if you have an infinite amount of data the true error is the same as the experimental error. In all other cases with a finite number of training molecules it is not. I would recommend to compare your result to other method based approaches, which is the most serious way to compare prediction rates. But as already said in [2] you should not use leave-one-out cross-validation for comparing feature selection methods, also not emprical selected feature sets.
[1] Wolpert, D. H. and Macready, W. G. No Free Lunch Theorems for Optimization IEEE Transactions on Evolutionary Computation, 1997 , 1 , 67-82 [2] Wegner, J. K. and Frvhlich, H. and Zell, A. Feature selection for Descriptor based Classification Models. 2. Human Intestinal Absorption (HIA) J. Chem. Inf. Comput. Sci., 2004 , 44 , 931-939 
 Kind regards, Joerg
I have a question about Correlation Coefficient of cross-validation. Can anyone tell me how much the value of Correlation Coefficient indicate the result is reliable? Certainly I know the larger the value, the more reliable the result. I want to know the lowest limit of that value. I read some papers that used the Correlation Coefficient value, some values are lower than 0.8, is it OK for present them in journals? Or it depends on different situation or topic? 
 Thanks a lot to you all,
 Holly
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 Dipl. Chem. Joerg K. Wegner
 Center of Bioinformatics Tuebingen (ZBIT)
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