Author |
Message |
Bilal Ahmad Malik (elp09bm)
Junior Member Username: elp09bm
Post Number: 7 Registered: 7-2011
| Posted on Monday, August 08, 2011 - 8:16 am: | |
Hi All, Could anyone please advice. Thanks, Bilal |
Bilal Ahmad Malik (elp09bm)
Junior Member Username: elp09bm
Post Number: 6 Registered: 7-2011
| Posted on Thursday, August 04, 2011 - 9:53 am: | |
Hi Barry, Thanks for your response. As you pointed out there looks to be something wrong.The above result were using support vector regression. But now I thought I will use PLs and I tried first using all the 90 samples for cross validation and got following results. RMSEC RMSECV Preprocessing LVS 19.5 27.19 Istderivative 7 27.4 67.32 2ndderivative 6 Then I did usual method of splitting the data into training and test. I used 60 for training and 30 for test. the results were as follows RMSEC RMSECV RMSEP Preprocessing LVS 29.13 36.4 37.3 Istderivative 6 20.9 70.8 82.39 2ndderivative 6 As you could see here in this model as well that I am getting better results with first derivative. if you need more information like the graphs I am getting I could send. |
Barry M. Wise (bmw)
Junior Member Username: bmw
Post Number: 7 Registered: 2-2011
| Posted on Wednesday, August 03, 2011 - 2:29 pm: | |
Hi Bilal: You wouldn't generally expect a model with second derivative to be consistently better (or worse) than one based on first derivative data. It depends on the data. If it you have only a constant baseline problem, then first derivative would probably be better, whereas if you have a problem with a sloping baseline, you'd expect second derivative to be better. In general, derivatives "blow up the noise," so you want to use the minimum derivative that works. Thanks for including the table. If your model based on first derivative has an RMSEC of 13.5 but a RMSECV of 34.5, then it is pretty over-fit. How many PLS or PCR factors did you use? You might actually get a better RMSEP on the test set if you backed off by one or two. You'd likely also wind up with a model that was a little more robust--less sensitive to instrument drifts and minor contaminants in future samples. Looks like either you dropped a digit in your table for the second derivative, or if not, your model is WAY over-fit with a RMSEC of 1.45 and RMSECV of 97.32. What else is different about this model besides the derivative? I'm guessing something..... BMW |
Bilal Ahmad Malik (elp09bm)
New member Username: elp09bm
Post Number: 5 Registered: 7-2011
| Posted on Wednesday, August 03, 2011 - 11:15 am: | |
I have NIR data which for which I am trying to develop a calibration model using PCR,PLS and SVR(support vector regression). My result show better SEP value when using Ist derivative followed by autoscale as compared to 2nd derivative though RMSEC is better when using 2nd derivative. I was expecting better SEP value as well when using second derivative as well. This happens in case of PLS,PCR,SVR. Could any one take the pains of explaining if it is acceptable behavior or is something wrong. I am using PLS_ToolBOx_621 for analysis. SVM RMSEC RMSECV RMSEP R^2Cal R^2Pred Preprocessing 13.5 34.5 20.39 0.99 0.95 Istderivative 1.45 97.32 82.39 0.99 0.78 2ndderivative Kindly help. |
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