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In this post we review different methods to compute prediction intervals, containing the next (unknown) observation with high probability and being at the heart of Conformal Prediction (CP). We will highlight that each method is characterized by a different and non-trivial trade-off between computational complexity, coverage properties and the size of the prediction interval. Scenario. We are…
Pimp quantile regression with strong coverage guarantees Suppose that we are given a historical dataset containing samples of the form , where and are the -th realizations of (predictor) variable and of (predicted) variable , respectively. As a running example, let us consider the following dataset: Our goal #1 is to estimate the trend of variable…
An expressive and robust alternative to least square For regression problems, least square regression (LSR) arguably gets the lion share of data scientists’ attention. The reasons are several: LSR is taught in virtually every introductory statistics course, it is intuitive and is readily available in most of software libraries. LSR estimates the mean of the predicted variable…
“A colleague of high repute asked an equally well-known colleague:— What would you say if you were told that the multi-armed bandit problem had been solved?— Sir, the multi-armed bandit problem is not of such a nature that it can be solved.” Peter Whittle In our busy daily life, while multi-tasking we are constantly faced…
The importance of being uncertainty-aware. When making a prediction (in a regression or a classification setting) based on observed inputs , it is often important to know which range the true value will fall in, with high confidence. For instance, a trader would be interested in knowing within which boundaries the stock price remains, rather than a…
ML without tears 