Locklin on science

Predicting with confidence: the best machine learning idea you never heard of

Posted in machine learning by Scott Locklin on December 5, 2016

One of the disadvantages of machine learning as a discipline is the lack of reasonable confidence intervals on a given prediction. There are all kinds of reasons you might want such a thing, but I think machine learning and data science practitioners are so drunk with newfound powers, they forget where such a thing might be useful. If you’re really confident, for example, that someone will click on an ad, you probably want to serve one that pays a nice click through rate. If you have some kind of gambling engine, you want to bet more money on the predictions you are more confident of. Or if you’re diagnosing an illness in a patient, it would be awfully nice to be able to tell the patient how certain you are of the diagnosis and what the confidence in the prognosis is.

There are various ad hoc ways that people do this sort of thing.  The one you run into most often is some variation on cross validation, which produces an average confidence interval. I’ve always found this to be dissatisfying (as are PAC approaches). Some people fiddle with their learners and in hopes of making sure the prediction is normally distributed, then build confidence intervals from that (or for the classification version, Platt scaling using logistic regression).  There are a number of ad hoc ways of generating confidence intervals using resampling methods and generating a distribution of predictions. You’re kind of hosed though, if your prediction is in online mode.  Some people build learners that they hope will produce a sort of estimate of the conditional probability distribution of the forecast; aka quantile regression forests and friends. If you’re a Bayesian, or use a model with confidence intervals baked in, you may be in pretty good shape. But let’s face it, Bayesian techniques assume your prior is correct, and that new points are drawn from your prior. If your prior is wrong, so are your confidence intervals, and you have no way of knowing this.  Same story with heteroscedasticity. Wouldn’t it be nice to have some tool to tell you how uncertain your prediction when you’re not certain of your priors or your algorithm, for that matter?



Well, it turns out, humanity possesses such a tool, but you probably don’t know about it. I’ve known about this trick for a few years now, through my studies of online and compression based learning as a general subject. It is a good and useful bag of tricks, and it verifies many of the “seat of the pants” insights I’ve had in attempting to build ad-hoc confidence intervals in my own predictions for commercial projects.  I’ve been telling anyone who listens for years that this stuff is the future, and it seems like people are finally catching on. Ryan Tibshirani, who I assume is the son of the more famous Tibshirani, has published a neat R package on the topic along with colleagues at CMU. There is one other R package out there and one in python. There are several books published in the last two years. I’ll do my part in bringing this basket of ideas to a more general audience, presumably of practitioners, but academics not in the know should also pay attention.

The name of this basket of ideas is “conformal prediction.” The provenance of the ideas is quite interesting, and should induce people to pay attention. Vladimir Vovk is a former Kolmogorov student, who has had all kind of cool ideas over the years. Glenn Shafer is also well known for his co-development of Dempster-Shafer theory, which is a brewing alternative to standard measure-theoretic probability theory which is quite useful in sensor fusion, and I think some machine learning frameworks. Alexander Gammerman is a former physicist from Leningrad, who, like Shafer, has done quite a bit of work in the past with Bayesian belief networks. Just to reiterate who these guys are: Vovk and Shafer have also previously developed a probability theory based on game theory which has ended up being very influential in machine learning pertaining to sequence prediction. To invent one new form of probability theory is clever. Two is just showing off! The conformal prediction framework comes from deep results in probability theory and is inspired by Kolmogorov and Martin-Lof’s ideas on algorithmic complexity theory.


The advantages of conformal prediction are many fold. These ideas assume very little about the thing you are trying to forecast, the tool you’re using to forecast or how the world works, and they still produce a pretty good confidence interval. Even if you’re an unrepentant Bayesian, using some of the machinery of conformal prediction, you can tell when things have gone wrong with your prior. The learners work online, and with some modifications and considerations, with batch learning. One of the nice things about calculating confidence intervals as a part of your learning process is they can actually lower error rates or use in semi-supervised learning as well. Honestly, I think this is the best bag of tricks since boosting; everyone should know about and use these ideas.

The essential idea is that a “conformity function” exists. Effectively you are constructing a sort of multivariate cumulative distribution function for your machine learning gizmo using the conformity function. Such CDFs exist for classical stuff like ARIMA and linear regression under the correct circumstances; CP brings the idea to machine learning in general, and to models like ARIMA  when the standard parametric confidence intervals won’t work. Within the framework, the conformity function, whatever may be, when used correctly can be guaranteed to give confidence intervals to within a probabilistic tolerance. The original proofs and treatments of conformal prediction, defined for sequences, is extremely computationally inefficient. The conditions can be relaxed in many cases, and the conformity function is in principle arbitrary, though good ones will produce narrower confidence regions. Somewhat confusingly, these good conformity functions are referred to as “efficient” -though they may not be computationally efficient.


The original research and proofs were done on so-called “transductive conformal prediction.” I’ll sketch this out below.

Suppose you have a data set Z:= z_1,...,z_N  , with z_i:=(x_i,y_i) where x_i has the usual meaning of a feature vector, and y_i the variable to be predicted. If the N! different possible orderings are equally likely, the data set Z is exchangeable. For the purposes of this argument, most data sets are exchangeable or can be made so. Call the set of all bags of points from Z with replacement a “bag” B .

The conformal predictor \Gamma^{\epsilon}(Z,x) := \{y | y^{p} > \epsilon \} where Z is the training set and x is a test object and \epsilon \in (0,1) is a defined probability of confidence in a prediction. If we have a function A(B,z_i) which measures how different a point z_i is the bag set of B .

Example: If we have a forecast technique which works on exchangeable data, \phi(B) , then a very simple function is the distance between the new point and the forecast based on the bag set. A(B,z_i):=d(\phi(B), z_i)  .

Simplifying the notation a little bit, let’s call A_i := A(B^{-i},z_i)  where B^{-i} is the bag set, missing z_i  . Remembering that bag sets B are sets of all the orderings of Z we can see that our p^y can be defined from the nonconformity measures; p^{y} := \frac{\#\{i=1,...,n|A_i \geq A_n \} }{n}  This can be proved in a fairly straightforward way. You can find it in any of the books and most of the tutorials.

Practically speaking, this kind of transductive prediction is computationally prohibitive and not how most practitioners confront the world. Practical people use inductive prediction, where we use training examples and then see how they do in a test set. I won’t go through the general framework for this, at least this time around; go read the book or one of the tutorials listed below. For one it is worth, one of the forms of Inductive Conformal Prediction is called Mondrian Conformal Prediction; a framework which allows for different error rates for different categories, hence all the Mondrian paintings I decorated this blog post with.


For many forms of inductive CP, the main trick is you must subdivide your training set into two pieces. One piece you use to train your model, the proper training set. The other piece you use to calculate your confidence region, the calibration set. You compute the non-conformity scores on the calibration set, and use them on the predictions generated by the proper training set. There are
other blended approaches. Whenever you use sampling or bootstrapping in  your prediction algorithm, you have the chance to build a conformal predictor using the parts of the data not used in the prediction in the base learner. So, favorites like Random Forest and Gradient Boosting Machines have computationally potentially efficient conformity measures. There are also flavors using a CV type process, though the proofs seem more weak for these. There are also reasonably computationally efficient Inductive CP measures for KNN, SVM and decision trees. The inductive “split conformal predictor” has an R package associated with it defined for general regression problems, so it is worth going over in a little bit of detail.
For coverage at \epsilon confidence, using a prediction algorithm \phi and training data set Z_i,i=1,...,n , randomly split the index i=1,...,n into two subsets, which as above, we will call the proper training set and the calibration set I_1,I_2 .

Train the learner using data on the proper training set I_1

\phi_{trained}:=\phi(Z_i); i \in I_1 . Then, using the trained learner, find the residuals in the calibration set:

R_i := |Y_i - \phi(X_i)|, i \in I_2 
d := the k th smallest value in \{R_i :i \in I_2\} where
k=(n/2 + 1)(1-\epsilon)

The prediction interval for a new point x is \phi(x)-d,\phi(x)+d

This type of thing may seem unsatisfying, as technically the bounds on it only exist for one predicted point. But there are workarounds using leave one out in the ranking. The leave one out version is a little difficult to follow in a lightweight blog, so I’ll leave it up as an exercise for those who are interested to read more about it in the R documentation for the package.

Conformal prediction is about 10 years old now: still in its infancy.  While forecasting with confidence intervals is inherently useful, the applications and extensions of the idea are what really tantalizes me about the subject. New forms of feature selection, new forms of loss function which integrate the confidence region, new forms of optimization to deal with conformal loss functions, completely new and different machine learning algorithms, new ways of thinking about data and probabilistic prediction in general. Specific problems which CP has had success with; face recognition, nuclear fusion research, design optimization, anomaly detection, network traffic classification and forecasting, medical diagnosis and prognosis, computer security, chemical properties/activities prediction and computational geometry. It’s probably only been used on a few thousand different data sets. Imagine being at the very beginning of Bayesian data analysis where things like the expectation maximization algorithm are just being invented, or neural nets before backpropagation: I think this is where the CP basket of ideas is at.  It’s an exciting field at an exciting time, and while it is quite useful now, all kinds of great new results will come of it.

There is a website and a book. Other papers and books can be found in the usual way. This paper goes with the R package mentioned above, and is particularly clearly written for the split and leave one out conformal prediction flavors. Here is a presentation with some open problems and research directions if you want to get to work on something interesting. Only 19 packages on github so far.

Get your Conformal Predictions here.

Get your Conformal Predictions here.


2 Responses

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  1. danofer said, on December 12, 2016 at 9:59 pm

    I’m hazy on how this is different from simply evaluating the error on a validation set drawn from thetraining set (and seperate from the test set)

    • Scott Locklin said, on December 12, 2016 at 11:12 pm

      Because it’s defined for out of sample points, and it changes when your out of sample points have a lower or higher non-conformity score. So you know things like, “I am really sure of my prediction on THIS point, but not so much on THAT point.”
      While this blog has introduced the idea to a broader audience, I confess it seems to have fallen well short of the mark. I’ll have to do another one with examples.

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