Dear Greg, > > I have added the code how I generate "my own confusion matrix" to the Wiki. > > In my understanding, my function uses the predictions from the out-of-bag > > prediction. But I guess that I have overlooked some nasty detail. > > You call: > pred,conf=cmp.ClassifyExample(pts[i]) > This uses the full composite model to make each prediction, it doesn't > do the same out-of-bag prediction done by > ScreenComposite.ShowVoteResults > > It is possible to get both the out-of-bag confusion matrix as a python > object (it's part of the return tuple from > ScreenComposite.ShowVoteResults) and the breakdown of the out-of-bag > predictions by point (not quite as straightforward, but possible). > What exactly are you trying to do?
I'm particularly interested in understanding why the model fingerprint-based performs rather bad. For this purpose, I would like to analyze the chemical structures for which the predictions went wrong or did not go wrong. > > > Cheers & Thanks, > > Paul > > > > > > P.S.: When comparing the results with a PipelinePilot-based Bayesian > > catagorization model (ECFP_4 & standard settings), I'm surprised to see > > that the PipelinePilot model is significantly better. I thought that the > > MorganFingerprints are comparable to the ECFPs and would have assumed that > > the model quality is in a similar range. > > It's probably not the fingerprints, but the model-building approach > that makes the difference here. You can test this if you want in knime > using the RDKit morgan fingerprints with the naive bayes fingerprint > learner they added in version 2.4. Just started a new thread in the knime forum :) Cheers, Paul This message and any attachment are confidential and may be privileged or otherwise protected from disclosure. If you are not the intended recipient, you must not copy this message or attachment or disclose the contents to any other person. If you have received this transmission in error, please notify the sender immediately and delete the message and any attachment from your system. Merck KGaA, Darmstadt, Germany and any of its subsidiaries do not accept liability for any omissions or errors in this message which may arise as a result of E-Mail-transmission or for damages resulting from any unauthorized changes of the content of this message and any attachment thereto. Merck KGaA, Darmstadt, Germany and any of its subsidiaries do not guarantee that this message is free of viruses and does not accept liability for any damages caused by any virus transmitted therewith. Click http://disclaimer.merck.de to access the German, French, Spanish and Portuguese versions of this disclaimer. ------------------------------------------------------------------------------ Got Input? Slashdot Needs You. Take our quick survey online. Come on, we don't ask for help often. Plus, you'll get a chance to win $100 to spend on ThinkGeek. http://p.sf.net/sfu/slashdot-survey _______________________________________________ Rdkit-discuss mailing list [email protected] https://lists.sourceforge.net/lists/listinfo/rdkit-discuss
