Some Thoughts on "Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?"

Sorry for the blogging break. I’ve got a few planned for the next few weeks based on some work I’ve been doing.

In the meantime, you should check out “Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?” by Manuel Fernandez-Delgado at JMLR. They took a large number of classifiers and ran them against a large number of data sets from UCI. I was a reviewer on this paper (it heavily relies on caret) and have been interested in seeing peoples reaction when it was made public.

My thoughts:

  • Before reading this manuscript, take a few minutes and read this oldie by David Hand.
  • Obviously, it pays to tune your model. That is not the most profound statement to make but the paper does a good job of quantifying the impact of tuning the hyper-parameters.

  • Random forest takes the top spot on a regular basis. I was surprised by this since, in my experience, boosting does better and bagging does almost as well.

  • The authors believe that the parallel version of random forest in caret had an edge in performance. That’s hard to believe since it does’t do anything more than split the forest across different cores. That’s it. I took it out of the package for a bit because, if you are tuning the model, parallelizing the cross-validation is faster. I put it back in a few verisons ago since I knew people would want it after reading this manuscript.

  • I was hoping that the authors would take a better graphical and analytical approach to comparing the methods. Table after table numbs my soul.

  • Despite the number of models used, it would have been nice to see more emphasis on recent deep-learning models and boosting via the gbm package.

Sample Mislabeling and Boosted Trees

A while back, I saw this post on StackExchange/Crossvalidated: "Does anyone know how well C5.0 boosting performs in the presence of mislabeled data?" I did some simulations in order to make a comparison with gradient boosting machines (GBM).

Some publications simulate mislabelling by sampling from distinct multivariate distirbutions for each class. I simulated two class data based on this post. Each simulated sample has an associated probability of being in class #1. A random uniform number is generated to assign each sample and observed class label. To mislabel X% of the data, a random set of samples are selected and the probability of being in class #1 is reversed.

Simulated data sets were simulated with training set sizes between 100 and 1000. The amount of mislabeled data also varied (at 5%, 10%, 15% and 20%). For each mislabeled data set, there is a matched training set (form the same random number seed) with no intentional mislabeling. For each of these configurations, 500 simulations were conducted.

Model performance was assessed using the area under the ROC curve. A test set of 10,000 with no mislabeling was used to evaluate performance.

For the C5.0 and GBM models, models were tuned using cross-validation. For each technique, a model was trained on the mislabeled data and another on the correctly labeled data. In this way, we have a "control" model that reflects how well the model does for each data set if there were no added noise due to mislabeling. As a result, for C5.0 and GBM, a percentage of performance loss can be calculated against the correctly labelled control set:

pct = (mislabeled - correct)/correct*100). 

This image contains the distributions of the percent loss across the configurations:


Some other observations:

  • When there is no mislabeling, the results are almost identical
  • Small amounts of mislabeling do not hurt either model very much
  • The loss of performance decreases with training set size
  • With gross amounts of mislabeling, the gradient boosting machine is not affected as much as C5.0
  • The effect of mislabeling on C5.0 also impacts the variation in the results. If you compare the columns above, note that the C5.0 distribution does not simply shift to the left with the same level of variation.

I contacted Ross Quinlan about this and his response was:

"I agree with your conclusions for the function that you studied. My experiments with noise and AdaBoost suggested that the effect of noise (mislabeling) varies markedly with different applications. There are some summary results in the first part of the following:

I have only some vague ideas about why thus might be. For those applications where the classes are well-separated in the attribute space, mislabeling does not seem to alter the class clusters much. Alternatively, for applications where there is a tight boundary between two classes, mislabeling could markedly affect the perceived class divide."