$$ \newcommand{\E}{\mathbb{E}} \newcommand{\bw}{\mathbf{w}} \newcommand{\bx}{\mathbf{x}} \newcommand{\intd}{\;\mathrm{d}} \newcommand{\la}{\!\leftarrow\!} \newcommand{\te}{\!=\!} \newcommand{\tm}{\!-\!} $$

Training, Testing, and Evaluating Different Models

You now have enough knowledge to be dangerous. You could read some data into matrices/arrays in a language like Matlab or Python, and attempt to fit a variety of linear regression models. They may not be sensible models, and so the results may not mean very much, but you can do it.

You could also use more sophisticated and polished methods baked into existing software. Many Kaggle competitions are won using the easy-to-use and fast XGBoost, or using ‘convolutional neural nets’, which you can fit with several packages, for example Keras.

How do you decide which method(s) to use, and work out what you can conclude from the results?


It is almost always a good idea to find the simplest possible algorithm that could possibly give an answer to your problem, and run it. In a regression task, you could report the mean square error of a constant function, \(f(x)\te b\). If you’re not beating that model on your training data, there is probably a bug in your code. If a fancy method does not generalize to new data as well as the baseline, then maybe the problem is simply too hard… or there are more subtle bugs in your code.

It is also usual to implement stronger baselines. For example, why use a neural network if straight-forward linear regression works better? If you are proposing a new method for an established task, you should try to compare to the existing state-of-the art if possible. (If a paper does not contain enough detail to be reproducible, and does not come with code, they may not deserve comparison.)

I’ve heard the following piece of advice for finding a machine learning algorithm to solve a practical problem1:

“Find a recent paper at a top machine learning conference, like NIPS or ICML that’s on the sort of task you need to solve. Look at the baseline that they compare their method to… and implement that.”

If they used a method for comparison, it should be somewhat sensible, but it’s probably a lot simpler and tested than the new method. And for many applications, you won’t actually need this year’s advance to achieve what you need.

Training and Testing Sets and Generalization Error

We may wish to compare a linear regression model, \(f(\bx;\bw,b)=\bw^\top\bx + b\), to a constant function baseline \(f(\bx;b)=b\). The linear regression model can never be beaten on the training data by the baseline, even if the data are random noise. The models are nested: linear regression can become the simpler model by setting \(\bw\te\mathbf{0}\), and obtain the same performance. Then there will almost always be some way to set \(\bw\!\ne\!\mathbf{0}\) to improve the square error slightly.

Thus we cannot use the performance on a training set to pick amongst models. The most complicated model will usually win, and it will always win if the models are nested. Yet in the previous note we saw how dramatically bad the fit can be from models with many parameters.

Instead, we could compare our model to a baseline on a testing set or test set. The test data should have been held out when fitting or training the model. The test data is only used to report the square error the model achieves when generalizing to new data.

Formally, the generalization error, is the average error or loss that the model function \(f\) would achieve on future test cases coming from some distribution \(p(\bx,y)\): \[ \text{Generalization error} = \E_{p(\bx,y)}[L(y, f(\bx))] = \int L(y, f(\bx))\,p(\bx,y)\intd\bx\intd y, \] where \(L(y, f(\bx))\) is the error we record if we predict \(f(\bx)\) but the output was actually \(y\). For example, if we care about square error, then \(L(y, f(\bx)) = (y\tm f(\bx))^2\).

We don’t have access to the distribution of data \(p(\bx,y)\), or we wouldn’t need to do learning. However, the average error on a test set of size \(M\), gives a Monte Carlo estimate of the integral above: \[ \text{Average test error} = \frac{1}{M} \sum_{m=1}^M L(y^{(m)}, f(\bx^{(m)})), \qquad \bx^{(m)},y^{(m)}\sim p(\bx,y). \] Assuming the test items are drawn from the distribution we are interested in, the expected value of the test error is equal to the generalization error. (Check you can see why.)

Reporting an average test error, rather than a total, usually means that the value is more interpretable, and the average error approaches a constant as the test set size \(M\) increases. For ease of comparison I usually also report an average training error; I divide the sum of training errors across all cases by the number of training cases.

Validation or Development sets

If we have many different alternative models we shouldn’t evaluate them all on the test set. Selecting the best model from many variants often picks a model that did well by chance, so its test performance will often be lower on future data. For example, if we test a set of models with different regularization constants \(\lambda\) and select the best, we have fit the parameter \(\lambda\) to the test set. However, the test set is meant for evaluation, and meant to be held-out from all training procedures.

Instead we should split the data not held out for testing into a training set and validation set. We fit the main parameters of a model, such as the weights of a regression model on the training set. Different variants of the model, perhaps with different regularization parameters, are then evaluated on the validation set to see which settings seem to generalize best. Finally, to report how well the selected model is likely to do on future data, it is evaluated on the test set.

Example: the plot below shows some polynomial fits to some training data (blue crosses) with different degrees \(p\). The quadratic curve is under-fitting, and the 9th order polynomial is showing clear signs of over-fitting.

The red dots are validation data. Training and validation errors were then plotted for polynomials of different orders:

As expected, the training error falls monotonically as we increase the complexity of the model. However, based on the validation performance we would pick a fifth order polynomial. The average error the fourth to seventh order polynomials all seem similar.

A more elaborate procedure called \(K\)-fold cross-validation is appropriate for small datasets, where holding out validation data ‘just’ for the selection of model choices leaves too little data for training a good model. The data that we can fit to is split into \(K\) parts. Each model is fitted \(K\) times, each with a different part used as a validation set, and the remaining \(K\tm1\) parts used for training. We select the model with the lowest validation error, when averaged over the \(K\) folds.

If there’s a really small dataset, a paper might report a \(K\)-fold score in place of a test set error. However, individual model choices should probably be performed using cross-validation nested within each fold of the outer procedure. Performing so many model fits could be rather costly! Bayesian procedures (considered later in the course) can become especially appealing in these data-limited settings.

Warning: Don’t fool yourself (or make a fool of yourself)

It is surprisingly easy to accidentally over-fit a model to the available test data, and then generalize poorly on future data. For example, someone may have followed good practice for all of their analysis, but then the final test score is disappointing. They then realize that there was something they should probably have done differently, so they change that and try again. Then they have another realization, but after that change the test score gets worse so they revert that change… and so on.

Each minor re-run of a method, or peek at the test set, doesn’t seem like it could cause any problems individually. But the effects build up. These problems with accidental over-fitting are frequently seen on Kaggle. Their competitions display a public leaderboard, based on a test set, but the final rankings are based on a second test set. It is common for some competitors to fall many places when the leaderboard is re-ranked. One such competitor wrote a reflective blog post on how they had fooled themselves. Despite knowing about cross-validation and the dangers of over-fitting, they slowly but surely slipped into fitting the test set. They reached second place on the public leaderboard, but fell dramatically when it was re-ranked… embarrassingly beneath one of the available baselines.

Training on the test set is one of the worst mistakes you can make in machine learning. At best, the results you present will be misleading, and at worst they will be meaningless. Markers of dissertation projects are likely to penalize such poor practice severely. In your own projects, if you can2, I suggest holding out some data that you never look at during the bulk of your research. Pretend it doesn’t exist. Then right at the end, test only the most-interesting models on the totally-held out test set.

Limitations of test set errors

Tests on held-out data provide straightforward quantitative comparisons between models. Based on these comparisons, if done properly, we can be fairly sure how well a model will generalize to data drawn from the same distribution.

Future data often won’t be drawn from the same distribution however. The properties of most systems drift over time. Dealing with these changes is hard, and often ignored. If your data is explicitly provided as a time-series, you may want to adopt a different training and testing procedure. For example, in online prediction, you predict each item one at a time in sequence, updating the model with each item after you have predicted it.

Even for stable systems, test set errors only say how well, under the given distribution, the models make predictions. A model with low test error does not necessarily reflect the structure of the real system, and as a result may generalize poorly if used in other ways. For example, you should be very careful about attempting to interpret meaning behind the weights in a model. Statistics books are full of examples of how various problems (e.g., selection effects, confounds, and model mismatch) can make you reach completely wrong conclusions about a system from a model’s fitted parameters.

Further Reading

The following two book sections contain (optional) more details. These sections may assume some knowledge we haven’t covered yet.

Keen students could read the following book chapter by Amos Storkey: When training and test sets are different: characterising learning transfer. In Dataset Shift in Machine Learning, Eds Candela, Sugiyama, Schwaighofer, Lawrence. MIT Press.


Aside: what is “over-fitting” exactly?

These notes described over-fitting as producing unreasonable fits that will generalize poorly, at the expense of fitting the training data unreasonably closely. Thus, models that are said to over-fit usually have lower training error than ‘good’ fits, yet higher generalization error.

I believe it is hard to crisply define over-fitting however. Mitchell’s Machine Learning textbook (1997, p67) attempts a definition:

“A hypothesis \(f\) is said to over-fit the data if there exists some alternative hypothesis \(f'\) such that \(f\) has a smaller training error than \(f'\), but \(f'\) has a smaller generalization error than \(f\).”

However, I think almost all models \(f\) that are ever fitted would satisfy these criteria! Thus the definition doesn’t seem useful to me.

If we think we have enough data to get a good fit to a system everywhere, then we expect the training and test errors to be similar. Thus one indication of possible over-fitting is that the average test error is much worse than the average training error. However, there is no hard rule here either. For example, if we know that our data are noiseless, we know exactly what the function should be at our training locations. It is therefore reasonable we will get lower error at or close to the training locations than at test locations in other places.

The reality is that there is no single continuum from under-fit models to over-fit models, that passes through an ideal model somewhere in between. I believe most large models will have some aspects that are overly simplified, yet other aspects that result from an unreasonable attempt to explain noise in the training items.

While I can’t define over-fitting precisely, the concept is still useful. Adding flexibility to models does often make test performance drop. It is a useful shorthand to say that we are selecting a smaller model, or applying a form of regularization, to prevent over-fitting.

  1. I wish I knew who to credit. The “quote” given is a paraphrase of something I’ve heard in passing.

  2. Sometimes this best practice is hard to realize.