Does Balancing Classes Improve Classifier Performance?

By Nina Zumel

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(This article was first published on Win-Vector Blog » R, and kindly contributed to R-bloggers)

It’s a folk theorem I sometimes hear from colleagues and clients: that you must balance the class prevalence before training a classifier. Certainly, I believe that classification tends to be easier when the classes are nearly balanced, especially when the class you are actually interested in is the rarer one. But I have always been skeptical of the claim that artificially balancing the classes (through resampling, for instance) always helps, when the model is to be run on a population with the native class prevalences.

On the other hand, there are situations where balancing the classes, or at least enriching the prevalence of the rarer class, might be necessary, if not desirable. Fraud detection, anomaly detection, or other situations where positive examples are hard to get, can fall into this case. In this situation, I’ve suspected (without proof) that SVM would perform well, since the formulation of hard-margin SVM is pretty much distribution-free. Intuitively speaking, if both classes are far away from the margin, then it shouldn’t matter whether the rare class is 10% or 49% of the population. In the soft-margin case, of course, distribution starts to matter again, but perhaps not as strongly as with other classifiers like logistic regression, which explicitly encodes the distribution of the training data.

So let’s run a small experiment to investigate this question.

Experimental Setup

We used the ISOLET dataset, available at the UCI Machine Learning repository. The task is to recognize spoken letters. The training set consists of 120 speakers, each of whom uttered the letters A-Z twice; 617 features were extracted from the utterances. The test set is another 30 speakers, each of whom also uttered A-Z twice.

Our chosen task was to identify the letter “n”. This target class has a native prevalence of about 3.8% in both test and training, and is to be identified from out of several other distinct co-existing populations. This is similar to a fraud detection situation, where a specific rare event has to be a population of disparate “innocent” events.

We trained our models against a training set where the target was present at its native prevalence; against training sets where the target prevalence was enriched by resampling to twice, five times, and ten times its native prevalence; and against a training set where the target prevalence was enriched to 50%. This replicates some plausible enrichment scenarios: enriching the rare class by a large multiplier, or simply balancing the classes. All training sets were the same size (N=2000). We then ran each model against the same test set (with the target variable at its native prevalence) to evaluate model performance. We used a threshold of 50% to assign class labels (that is, we labeled the data by the most probable label). To get a more stable estimate of how enrichment affected performance, we ran this loop ten times and averaged the results for each model type.

We tried three model types:

  • cv.glmnet from R package glmnet: Regularized logistic regression, with alpha=0 (L2 regularization, or ridge). cv.glmnet chooses the regularization penalty by cross-validation.
  • randomForest from R package randomForest: Random forest with the default settings (500 trees, nvar/3, or about 205 variables drawn at each node).
  • ksvm from R pacakge kernlab: Soft-margin SVM with the radial basis kernel and C=1

Since there are many ways to resample the data for enrichment, here’s how I did it. The target variable is assumed to be TRUE/FALSE, with TRUE as the class of interest (the rare one). dataf is the data frame of training data, N is the desired size of the enriched training set, and prevalence is the desired target prevalence.

makePrevalence = function(dataf, target, 
                          prevalence, N) {
  # indices of T/F
  tset_ix = which(dataf[[target]])
  others_ix = which(!dataf[[target]])
  
  ntarget = round(N*prevalence)
  
  heads = sample(tset_ix, size=ntarget, 
                 replace=TRUE)
  tails = sample(others_ix, size=(N-ntarget), 
                 replace=TRUE)
  
  dataf[c(heads, tails),]
}

Training at the Native Target Prevalence

Before we run the full experiment, let’s look at how each of these three modeling approaches does when we fit models the obvious way — where the training and test sets have the same distribution:

## [1] "Metrics on training data"
## accuracy precision   recall specificity         label
##   0.9985 1.0000000 0.961039     1.00000      logistic
##   1.0000 1.0000000 1.000000     1.00000 random forest
##   0.9975 0.9736842 0.961039     0.99896           svm
## [1] "Metrics on test data"
##  accuracy precision    recall specificity         label
## 0.9807569 0.7777778 0.7000000   0.9919947      logistic
## 0.9717768 1.0000000 0.2666667   1.0000000 random forest
## 0.9846055 0.7903226 0.8166667   0.9913276           svm

We looked at four metrics. Accuracy is simply the fraction of datums classified correctly. Precision is the fraction of datums classified as positive that really were; equivalently, it’s an estimate of the conditional probability of a datum being in the positive class, given that it was classified as positive. Recall (also called sensitivity or the true positive rate) is the fraction of positive datums in the population that were correctly identified. Specificity is the true negative rate, or one minus the false positive rate: the number of negative datums correctly identified as such.

As the table above shows, random forest did perfectly on the training data, and the other two did quite well, too, with nearly perfect precision/specificity and high recall. However, random forest’s recall plummeted on the hold-out set, to 27%. The other two models degraded as well (logistic regression more than SVM), but still manage to retain decent recall, along with good precision and specificity. Random forest also has the lowest accuracy on the test set (although 97% still looks pretty good — another reason why accuracy is not always a good metric to evaluate classifiers on. In fact, since the target prevalence in the data set is only about 3.8%, a model that always returned FALSE would have an accuracy of 96.2%!).

One could argue that if precision is the goal, then random forest is still in the running. However, remember that the goal here is to identify a rare event. In many such situations (like fraud detection) one would expect that high recall is the most important goal, as long as precision/specificity are still reasonable.

Let’s see if enriching the target class prevalence during training improves things.

How Enriching the Training Data Changes Model Performance

First, let’s look at accuracy.

The x-axis is the prevalence of the target in the training data; the y-axis gives the accuracy of the model on the test set (with the target at its native prevalence), averaged over ten draws of the training set. The error bars are the bootstrap estimate of the 98% confidence interval around the mean, and the values for the individual runs appear as transparent dots at each value. The dashed horizontal represents the accuracy of a model trained at the target class’s true prevalence, which we’ll call the model’s baseline performance. Logistic regression degraded the most dramatically of the three models as target prevalence increased. SVM degraded only slightly. Random forest improved, although its best performance (when training at about 19% prevalence, or five times native prevalence) is only slightly better than SVM’s baseline performance, and its performance at 50% prevalence is worse than the baseline performance of the other two classifiers.

Logistic regression’s degradation should be no surprise. Logistic regression optimizes deviance, which is strongly distributional; in fact, logistic regression (without regularization) preserves the marginal probabilities of the training data. Since logistic regression is so well calibrated to the training distribution, changes in the distribution will naturally affect model performance.

The observation that SVM’s accuracy stayed very stable is consistent with my surmise that SVM’s training procedure is not strongly dependent on the class distributions.

Now let’s look at precision:

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All of the models degraded on precision, random forest the most dramatically (since it started at a higher baseline), SVM the least. SVM and logistic regression were comparable at baseline.

Let’s look at recall:

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Enrichment improved the recall of all the classifiers, random forest most dramatically, although its best performance, at 50% enrichment, is not really any better than SVM’s baseline recall. Again, SVM’s recall moved the least.

Finally, let’s look at specificity:

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Enrichment degraded all models’ specificity (i.e. they all make more false positives), logistic regression’s the most dramatically, SVM’s the least.

The Verdict

Based on this experiment, I would say that balancing the classes, or enrichment in general, is of limited value if your goal is to apply class labels. It did improve the performance of random forest, but mostly because random forest was a rather poor choice for this problem in the first place (It would be interesting to do a more comprehensive study of the effect of target prevalence on random forest. Does it often perform poorly with rare classes?).

Enrichment is not a good idea for logistic regression models. If you must do some enrichment, then these results suggest that SVM is the safest classifier to use, and even then you probably want to limit the amount of enrichment to less than five times the target class’s native prevalence — certainly a far cry from balancing the classes, if the target class is very rare.

The Inevitable Caveats

The first caveat is that we only looked at one data set, only three modeling algorithms, and only one specific implementation of each of these algorithms. A more thorough study of this question would consider far more datasets, and more modeling algorithms and implementations thereof.

The second caveat is that we were specifically supplying class labels, using a threshold. I didn’t show it here, but one of the notable issues with the random forest model when it was applied to hold-out was that it no longer scored the datums along the full range of 0-1 (which it did, on the training data); it generally maxed out at around 0.6 or 0.7. This possibly makes using 0.5 as the threshold suboptimal. The following graph was produced with a model trained with the target class at native prevalence, and evaluated on our test set.

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The x-axis corresponds to different thresholds for setting class labels, ranging between 0.25 (more permissive about marking datums as positive) and 0.75 (less permissive about marking datums as classifiers). You can see that the random forest model (which didn’t score anything in the test set higher than 0.65) would have better accuracy with a lower threshold (about 0.3). The other two models have fairly close to optimal accuracy at the default threshold of 0.5. So perhaps it’s not fair to look at the classifier performance without tuning the thresholds. However, if you’re tuning a model that was trained on enriched data, you still have to calibrate the threshold on un-enriched data — in which case, you might as well train on un-enriched data, too. In the case of this random forest model, its best accuracy (at threshold=0.3) is about as good as random forest’s accuracy when trained on a balanced data set, again suggesting that balancing the training set doesn’t contribute much. Tuning the threshold may be enough.

However, suppose we don’t need to assign class labels? Suppose we only need the score to sort the datums, hoping to sort most of the items of interest to the top? This could be the case when prioritizing transactions to be investigated as fraudulent. The exact fraud score of a questionable transaction might not matter — only that it’s higher than the score of non-fraudulent events. In this case, would enrichment or class balancing improve? I didn’t try it (mostly because I didn’t think of it until halfway through writing this), but I suspect not.

Conclusions

  • Balancing class prevalence before training a classifier does not across-the-board improve classifier performance.
  • In fact, it is contraindicated for logistic regression models.
  • Balancing classes or enriching target class prevalence may improve random forest classifiers.
  • But random forest models may not be the best choice for very unbalanced classes.
  • If target class enrichment is necessary (perhaps because of data scarcity issues), SVM may be the safest choice for modeling.

A knitr document of our experiment, along with the accompanying R markdown file, can be downloaded here, along with a copy of the ISOLET data.

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