I am currently working on a RandomForest based prediction method using protein sequence data. I have generated two models first model (NF) using standard set of features and the second model (HF) using hybrid features. I have done Mathews Correlation Coefficient (MCC) and Accuracy calculation and the following are my results:

Model 1 (NF): Training Accuracy - 62.85% Testing Accuracy - 56.38 MCC - 0.1673

Model 2 (HF): Training Accuracy - 60.34 Testing Accuracy - 61.78 MCC - 0.1856

The testing data is an independent dataset (means not included in the training data).

Since there is a trade-off in accuracy and MCC between the models am confused about the prediction power of the models. Could you please share your thoughts on which model I should consider for further analysis? Apart from Accuracy and MCC is there any other measure that I should consider for validation?

Thanks in advance.


I like the idea of parsimony- the smaller the number of variables in the model, the better. Unless you are driven theoretically of course. Feature selection refers to the process of choosing which variables to use in the model (getting the best combination of variables). There are lots of different options for feature selection (worth a read). With that said, there should be inbuilt within the rf algorithm, a variable importance measure that you can generate as a starting point (with that said, be very very careful with this because there are noted biases in this) - see Strobl et al in the R journal.

I trust you have varied the number of variables randomly sampled at each node (this is mtry in R) and the depth of the trees and splitting criteria etc.

In terms of appearance, the second model looks slighly better to me, simply because of the reproduced accuracy in the test and train results. It always concerns me that if my test set accuracy is notably lower, there may be something wrong with the model. I trust you have made sure that your test and train set are balanced, at least on the dependent variable you are looking to classify. If this is binary (0,1) your models are not really doing much better than chance (50,50).

An very important thing to look at is the sensitivity (the number of true positives in a binary task 0,1 that are correctly classified) and specificity (the number of true negatives in a binary task 0,1) that are both correctly classified.

If possible, I would compare this model against other machine learning algorithms such as boosted trees, support vector machines (which do ok in gene data) etc.

I am not sure what package you are using - hope that helps if

If you are using r - look up caret in cran (really good intro to some of the ideas here and great for getting out some alternative measures of performance).

Paul D

  • $\begingroup$ Thanks Paul for your detailed answer. I will up-vote it once I have enough points :) ! Features in HF is around 100, but NF is around 500. I have tried feature selection using information gain method. $\endgroup$ – Khader Shameer Dec 3 '10 at 5:06
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    $\begingroup$ @Khader RF importance is more reliable than IG. $\endgroup$ – user88 Dec 3 '10 at 9:25
  • $\begingroup$ @Khader And a moderator tip: you can (and should) accept the best (in your view) answer by clicking the tick mark below voting arrows. This will give you 2 points and 15 extra points to the answerer. The accepted answer will be sticked to the top so that each new reader of a question would notice it first. $\endgroup$ – user88 Dec 3 '10 at 9:45
  • $\begingroup$ @mbq: Thanks for your tip. I will wait for a day or two before chosing the final answer. $\endgroup$ – Khader Shameer Dec 3 '10 at 16:14

It just seems those two variants are equivalent; yet some better test should be made to confirm this, at least cross validation.
Also if this NF and HF sets have some attributes in common, it may suggest that only this common part is useful -- I would invest some time in making feature selection.

  • $\begingroup$ Mbq: thanks for sharing your thoughts. I have tried a k-fold cross-validation and the results are average of the k-runs. HF is a subset of NF, but several features are merged as one single feature. $\endgroup$ – Khader Shameer Dec 3 '10 at 5:11

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