Is an overfitted model necessarily useless?

Assume that a model has 100% accuracy on the training data, but 70% accuracy on the test data. Is the following argument true about this model?

It is obvious that this is an overfitted model. The test accuracy can be enhanced by reducing the overfitting. But, this model can still be a useful model, since it has an acceptable accuracy for the test data.

• If 70% is acceptable in the particular applications, then I agree with you. May 11, 2017 at 6:37
• I'd fully agree with @RichardHardy. Take, for instance, a random forest: Often, by construction, the insample performance (not the out-of-bag performance) is close to 100%, so grossly overfitting. But still, the lower performance evaluated out-of-bag or on test/validation sets might be high enough to make it a useful model. May 11, 2017 at 7:28
• @Metariat Why not? This accuracy obtained on test set which is not used in the training phase. May 11, 2017 at 12:07
• @Metariat, Such an argument can take down most of statistical practice, e.g. the OLS estimator in the Normal linear model, $t$-test for equality of a sample mean to a hypothesized value, and what not. What is interesting is that the argument does not depend on the model overfitting on the training sample. It holds as well for underfit models and any model in general. Is that not correct? May 11, 2017 at 13:45
• I wouldn't call that model overfitted. An overfitted model is established comparing validation performance to test performance. Even then, only if the test performance was considerably lower than acceptable, possibly catastrophically. Training performance means nothing. May 11, 2017 at 13:55

I think the argument is correct. If 70% is acceptable in the particular application, then the model is useful even though it is overfitted (more generally, regardless of whether it is overfitted or not).

While balancing overfitting against underfitting concerns optimality (looking for an optimal solution), having satisfactory performance is about sufficiency (is the model performing well enough for the task?). A model can be sufficiently good without being optimal.

Edit: after the comments by Firebug and Matthew Drury under the OP, I will add that to judge whether the model is overfitted without knowing the validation performance can be problematic. Firebug suggests comparing the validation vs. the test performance to measure the amount of overfitting. Nevertheless, when the model delivers 100% accuracy on the training set without delivering 100% accuracy on the test set, it is an indicator of possible overfitting (especially so in the case of regression but not necessarily in classification).

• You asked for an example: take the code for a neural net on the iris dataset at stats.stackexchange.com/a/273930/2958 and then try with set.seed(100) for an illustration like the phenomenon described here and set.seed(15) for the opposite. Perhaps better to say "an indicator of possible overfitting" May 11, 2017 at 17:04
• Is it ever possible for a model to attain 100% accuracy on both train and test and has no overifitted ? Oct 6, 2019 at 5:57
• @Breeze, I think you could ask this on a separate thread (and link to this one for context if needed). Oct 6, 2019 at 7:53
• I just did here is the link Oct 6, 2019 at 8:20

In my past project with Credit Card Fraud detection, we intentionally want to over fit the data / hard coded to remember fraud cases. (Note, overfitting one class is not exactly the general overfitting problem OP talked about.) Such system has relatively low false positives and satisfy our needs.

So, I would say, overfitted model can be useful for some cases.

• This answer is quite interesting as it presents a use case. I think with "hard-coded to remember" @hxd1011 means that the model made sure that each of the reported fraud cases lead to a "fraud flag" and that they were not smoothed or interpolated away by a, uhm, say, fitted function. Something like that, right? May 11, 2017 at 15:08
• @IcannotFixThis yes. In fact, we tried many other ways to control false positive. But trying to overfit fraud cases, in crazy way worked well. May 11, 2017 at 15:10
• In your case, your evaluation function is overweighting TPR and underweighting FPR, e.g. F_$\beta$ score with $\beta$>>1. (Now I know why my debit card company is so annoying, they flag any little thing, even faulty card-scanners at gas stations)
– smci
May 11, 2017 at 23:16
• That may be annoying, but it is thousands of times less annoying than having your financed ruined because someone nefarious got your card information. Aug 25, 2017 at 14:56

Maybe: beware. When you say that 70% accuracy (however you measure it) is good enough for you, it feels like you're assuming that errors are randomly or evenly distributed.

But one of the ways of looking at overfitting is that it happens when a model technique allows (and its training process encourages) paying too much attention to quirks in the training set. Subjects in the general population that share these quirks may have highly-unbalanced results.

So perhaps you end up with a model that says all red dogs have cancer -- because of that particular quirk in your training data. Or that married people between the ages of 24 and 26 are nearly guaranteed to file fraudulent insurance claims. Your 70% accuracy leaves a lot of room for pockets of subjects to be 100% wrong because your model is overfit.

(Not being overfit isn't a guarantee that you won't have pockets of wrong predictions. In fact an under-fit model will have swaths of bad predictions, but with overfitting you know you are magnifying the effect of quirks in your training data.)

• Thanks. Do you mean that it is possible that this 70% accuracy is obtained on the quirks in the training data that is also available in the test data? Cannot we judge based on the accuracy of the test data? I think the quirks in the training data that are available in the test data should be learned during training. May 12, 2017 at 19:29
• If I understand you, it would be the opposite: overfitting to quirks has given you your high accuracy in training. The reason you get a lower accuracy in testing is that those quirks don't apply to your overall dataset. But of course your training and testing sets -- even if you do cross-validation, which helps -- may be quirky in relation to your population. In which case your testing/validation results won't well-predict how you actually perform. May 12, 2017 at 20:06
• You are right that the testing set may be quirky in relation to the population, but this is not specific to the overfited models. All of our evaluations suffer from it and we have no other choice than relying on the test set as a proxy for the true population. May 13, 2017 at 1:44
• True, it's not unique to overfitted models, but it is amplified in an overfit model. I want to say by definition the model is overfit because it clearly suffers from overemphasizing the quirks. May 13, 2017 at 2:20

No they can be useful, but it depends on your purpose. Several things spring to mind:

1. Cost-Sensitive Classification: If your evaluation function overweights TPR and underweights FPR, we use $$F_\beta$$ score with $$\beta \gg 1$$. (such as @hxd1011's answer on antifraud)

2. Such a classifier can be really useful in an ensemble. We could have one classifier with normal weights, one that overweights TPR, one that overweights FNR. Then even simple rule-of-three voting, or averaging, will give better AUC than any single best classifier. If each model uses different hyperparameters (or subsampled training-sets, or model architectures), that buys the ensemble some immunity from overfitting.

3. Similarly, for real-time anti-spam, anti-fraud or credit-scoring, it's ok and desirable to use a hierarchy of classifiers. The level-1 classifiers should evaluate really fast (ms) and it's ok to have a high FPR; any mistakes they make will be caught by more accurate, fully-featured, slower higher-level classifiers or ultimately human reviewers. Obvious example: prevent fake-news headlines from Twitter account takeovers like the 2013 "White House bomb attack kills three" from affecting \$billions of trading within ms of posting. It's ok for the level-1 classifier to flag that as positive for spam; let's allow it takes a little while to (automatically) determine the truth/falsehood of sensational-but-unverified news reports.

I'm not denying that an overfitted model could still be useful. But just keep in mind that this 70% could be a misleading information. What you need in order to judge if a model is useful or not is the out-of-sample error, not the testing error (the out-of-sample error is not known, so we have to estimate it using a blinded testing set), and that 70% is barely the good approximation.

In order to make sure that we're on the same page on the terminology after the comment of @RichardHardy, let's define the testing error as the error obtained when applying the model on the blind testing set. And the out-of-sample error is the error when applying the model to the whole population.

The approximation of the out-of-sample error depends on two things: the model itself and the data.

• An "optimal" model yields to an (testing) accuracy that scarcely depends on the data, in this case, it would be a good approximation. "Regardless" of the data, the prediction error would be stable.

• But, an overfitted model's accuracy is highly dependent of the data (as you mentioned 100% on the training set, and 70% on the other set). So it might happens that when applying to another data set, the accuracy could be somewhere lower than 70% (or higher), and we could have bad surprises. In other words, that 70% is telling you what you believe it to be, but it is not.

• Since the used test set that obtains 70% accuracy is not seen in the training phase, is not it a good estimation of the out-of-sample error? I think the difference between training error (100%) and testing error (70%) is not a good indication of the difference between out-of-sample error and test error. It is possible that the overfitted model performs 70% accurate in the real world, while it is 100% accurate for the training data. I expect training error to be lower than test error, since the training data are used to generate the model, but the test data are not seen during training. May 11, 2017 at 12:38
• I think you might have confused the validation set and the test set, and their respective errors. The test error is the out-of-sample error. While validation error is an optimistic measure of a selected model, test error is not. The test error is an unbiased estimate of how the model will perform on a new sample from the same population. We can estimate the variance of the test error, so we are quite fine by knowing only the test error as long as the test set is not too small. @Hossein May 11, 2017 at 12:49
• Can you elaborate on the difference between out-of-sample error and testing error? From my understanding, both are the error found when applying the model to samples not used to train the model. The only possible difference I can see is when using time-series data, the out-of-sample data should be from later time points, but this questions makes no mention of that. May 11, 2017 at 12:49
• From my perspective, testing error is the error obtained when applying the model into a blinded set, it is an approximation of the out-of-sample error, which is the error obtained when applying the model into the whole population. And it is not the same, the valuable information is the out-of-sample error. And when the model is overfitted, the testing error is not stable, and bad supprises could happen on the other data sets. May 11, 2017 at 13:08
• @Metariat, you are right that the test error is an estimate and it could be different from one test set to another. However, as I mentioned before, there is no reason to expect that the test error underestimates the true error (it does not, on average). So by taking a large-enough test sample, we can bound the test error with a desired level of confidence at a desired range. Now more practically, perhaps you should define the test error by editing your answer to make sure there is no misunderstanding of what you mean when contrasting the test error with the out-of-sample error. May 11, 2017 at 13:17