As John mentioned in his last post, we have been quite interested in the recent study by Fernandez-Delgado, et.al., “Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?” (the “DWN study” for short), which evaluated 179 popular implementations of common classification algorithms over 120 or so data sets, mostly from the UCI Machine Learning Repository. For fun, we decided to do a follow-up study, using their data and several classifier implementations from `scikit-learn`

, the Python machine learning library. We were interested not just in classifier accuracy, but also in seeing if there is a “geometry” of classifiers: which classifiers produce predictions patterns that look similar to each other, and which classifiers produce predictions that are quite different? To examine these questions, we put together a Shiny app to interactively explore how the relative behavior of classifiers changes for different types of data sets.

# A comment on preparing data for classifiers

I have been working through (with some honest appreciation) a recent article comparing many classifiers on many data sets: “Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?” Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, Dinani Amorim; 15(Oct):3133−3181, 2014 (which we will call “the DWN paper” in this note). This paper applies 179 popular classifiers to around 120 data sets (mostly from the UCI Machine Learning Repository). The work looks good and interesting, but we do have one quibble with the data-prep on 8 of the 123 shared data sets. Given the paper is already out (not just in pre-print) I think it is appropriate to comment publicly. Continue reading A comment on preparing data for classifiers

# Great new post by Win-Vector’s Nina Zumel

Win-Vector LLC’s Nina Zumel has a great new article on the issue of taste in design and problem solving: Design, Problem Solving, and Good Taste. I think it is a *big* issue: how can you expect good work if you can’t even discuss how to tell good from bad?

# Excel spreadsheets are hard to get right

Any practicing data scientist is going to eventually have to work with a data stored in a Microsoft `Excel`

spreadsheet. A lot of analysts use this format, so if you work with others you are going to run into it. We have already written how we don’t recommend using `Excel`

-like formats to exchange data. But we know if you are going to work with others you are going to have to make accommodations (we even built our own modified version of `gdata`

‘s underlying `Perl`

script to work around a bug).

But one thing that continues to confound us is how hard it is to read `Excel`

data correctly. When `Excel`

exports into `CSV/TSV`

style formats it uses fairly clever escaping rules about quotes and new-lines. Most `CSV/TSV`

readers fail to correctly implement these rules and often fail on fields that contain actual quote characters, separators (tab or comma), or new-lines. Another issue is `Excel`

itself often transforms data without any user verification or control. For example: `Excel`

routinely turns date-like strings into time since epoch (which it then renders as a date). We recently ran into another uncontrollable `Excel`

transform: changing the strings “`TRUE`

” and “`FALSE`

” into 1 and 0 inside the actual “`.xlsx`

” file. That is `Excel`

does not faithfully store the strings “`TRUE`

” and “`FALSE`

” even in its native format. Most `Excel`

users do not know about this, so they certainly are in no position to warn you about it.

This would be a mere annoyance, except it turns out `Libre Office`

(or at least LibreOffice_4.3.4_MacOS_x86-64) has a severe and silent data mangling bug on this surprising Microsoft boolean type.

We first ran into this in client data (and once the bug triggered it seemed to alter most of the columns), but it turns out the bug is very easy to trigger. In this note we will demonstrate the data representation issue and bug. Continue reading Excel spreadsheets are hard to get right

# Can we try to make an adjustment?

In most of our data science teaching (including our book Practical Data Science with R) we emphasize the deliberately easy problem of “exchangeable prediction.” We define exchangeable prediction as: given a series of observations with two distinguished classes of variables/observations denoted “x”s (denoting control variables, independent variables, experimental variables, or predictor variables) and “y” (denoting an outcome variable, or dependent variable) then:

- Estimate an approximate functional relation
`y ~ f(x)`

. - Apply that relation to new instances where
`x`

is known and`y`

is not yet known.

An example of this would be to use measured characteristics of online shoppers to predict if they will purchase in the next month. Data more than a month old gives us a training set where both `x`

and `y`

are known. Newer shoppers give us examples where only `x`

is currently known and it would presumably be of some value to estimate `y`

or estimate the probability of different `y`

values. The problem is philosophically “easy” in the sense we are not attempting inference (estimating unknown parameters that are not later exposed to us) and we are not extrapolating (making predictions about situations that are out of the range of our training data). All we are doing is essentially generalizing memorization: if somebody who shares characteristics of recent buyers shows up, predict they are likely to buy. We repeat: we are *not* forecasting or “predicting the future” as we are not modeling how many high-value prospects will show up, just assigning scores to the prospects that do show up.

The reliability of such a scheme rests on the concept of exchangeability. If the future individuals we are asked to score are exchangeable with those we had access to during model construction then we expect to be able to make useful predictions. How we construct the model (and how to ensure we indeed find a good one) is the core of machine learning. We can bring in any big name machine learning method (deep learning, support vector machines, random forests, decision trees, regression, nearest neighbors, conditional random fields, and so-on) but the legitimacy of the technique pretty much stands on some variation of the idea of exchangeability.

One effect antithetical to exchangeability is “concept drift.” Concept drift is when the meanings and distributions of variables or relations between variables changes over time. Concept drift is a killer: if the relations available to you during training are thought not to hold during later application then you should not expect to build a useful model. This one of the hard lessons that statistics tries so hard to quantify and teach.

We know that you should always prefer fixing your experimental design over trying a mechanical correction (which can go wrong). And there are no doubt “name brand” procedures for dealing with concept drift. However, data science and machine learning practitioners are at heart tinkerers. We ask: can we (to a limited extent) attempt to directly correct for concept drift? This article demonstrates a simple correction applied to a deliberately simple artificial example.

Image: Wikipedia: Elgin watchmaker

# Bias/variance tradeoff as gamesmanship

Continuing our series of reading out loud from a single page of a statistics book we look at page 224 of the 1972 Dover edition of Leonard J. Savage’s “The Foundations of Statistics.” On this page we are treated to an example attributed to Leo A. Goodman in 1953 that illustrates how for normally distributed data the maximum likelihood, unbiased, and minimum variance estimators of variance are in fact typically three different values. So in the spirit of gamesmanship you always have at least two reasons to call anybody else’s estimator incorrect. Continue reading Bias/variance tradeoff as gamesmanship

# Win-Vector LLC’s John Mount at Strata + Hadoop World October 2014

Win-Vector LLC‘s John Mount will be speaking at Strata + Hadoop World 2014 this month. Please attend my panel on data inventories (a key driver of data science project success) and attend my “Practical Data Science with R” book office hour (get your book signed!). Thank you both O’Reilly Media, Inc. and Waterline Data Science for making this possible.

Current schedule/location details after the click. Continue reading Win-Vector LLC’s John Mount at Strata + Hadoop World October 2014

# Estimating Generalization Error with the PRESS statistic

As we’ve mentioned on previous occasions, one of the defining characteristics of data science is the emphasis on the availability of “large” data sets, which we define as “enough data that statistical efficiency is not a concern” (note that a “large” data set need not be “big data,” however you choose to define it). In particular, we advocate the use of hold-out data to evaluate the performance of models.

There is one caveat: if you are evaluating a series of models to pick the best (and you usually are), then a single hold-out set is strictly speaking not enough. Hastie, et.al, say it best:

Ideally, the test set should be kept in a “vault,” and be brought out only at the end of the data analysis. Suppose instead that we use the test-set repeatedly, choosing the model with smallest test-set error. Then the test set error of the final chosen model will underestimate the true test error, sometimes substantially.

*The Elements of Statistical Learning*, 2nd edition.

The ideal way to select a model from a set of candidates (or set parameters for a model, for example the regularization constant) is to use a training set to train the model(s), a calibration set to select the model or choose parameters, and a test set to estimate the generalization error of the final model.

In many situations, breaking your data into three sets may not be practical: you may not have very much data, or the the phenomena you’re interested in are rare enough that you need a lot of data to detect them. In those cases, you will need more statistically efficient estimates for generalization error or goodness-of-fit. In this article, we look at the PRESS statistic, and how to use it to estimate generalization error and choose between models.

Continue reading Estimating Generalization Error with the PRESS statistic

# Factors are not first-class citizens in R

The primary user-facing data types in the R statistical computing environment behave as vectors. That is: one dimensional arrays of scalar values that have a nice operational algebra. There are additional types (lists, data frames, matrices, environments, and so-on) but the most common data types are vectors. In fact vectors are so common in R that scalar values such as the number `5`

are actually represented as length-1 vectors. We commonly think about working over vectors of “logical”, “integer”, “numeric”, “complex”, “character”, and “factor” types. However, a “factor” is not a R vector. In fact “factor” is *not* a first-class citizen in R, which can lead to some ugly bugs.

For example, consider the following R code.

```
```levels <- c('a','b','c')
f <- factor(c('c','a','a',NA,'b','a'),levels=levels)
print(f)
## [1] c a a <NA> b a
## Levels: a b c
print(class(f))
## [1] "factor"

This example encoding a series of 6 observations into a known set of factor-levels (`'a'`

, `'b'`

, and `'c'`

). As is the case with real data some of the positions might be missing/invalid values such as `NA`

. One of the strengths of R is we have a uniform explicit representation of bad values, so with appropriate domain knowledge we can find and fix such problems. Suppose we knew (by policy or domain experience) that the level `'a'`

was a suitable default value to use when the actual data is missing/invalid. You would think the following code would be the reasonable way to build a new revised data column.

```
```fRevised <- ifelse(is.na(f),'a',f)
print(fRevised)
## [1] "3" "1" "1" "a" "2" "1"
print(class(fRevised))
## [1] "character"

Notice the new column `fRevised`

is an absolute mess (and not even of class/type factor). This sort of fix would have worked if `f`

had been a vector of characters or even a vector of integers, but for factors we get gibberish.

We are going to work through some more examples of this problem. Continue reading Factors are not first-class citizens in R

# What is a win vector?

From time to time we are asked “what is the company name Win-Vector LLC referring to?” It is a cryptic pun trying to be an encoding of “we deliver victory.”

The story is an inside joke referring to something really only funny to one of the founders. But a joke that amuses the teller is always enjoyed by at least one person. Win-Vector LLC’s John Mount had the honor of co-authoring a 1997 paper titled “The Polytope of Win Vectors.” The paper title is obviously mathematical terms in an odd combination. However the telegraphic grammar is coincidentally similar to deliberately ungrammatical gamer slang such as “full of win” and “so much win.”

If we treat “win” as a concrete noun (say something you can put in a sack) and “vector” in its *non-mathematical* sense (as an entity of infectious transmission) we have “Win-Vector LLC is an infectious delivery of victory.” I.e.: we deliver success to our clients. Of course, we have now attempt to explain a weak joke. It is not as grand as “winged victory,” but it does encode a positive company value: Win-Vector LLC delivers successful data science projects and training to clients.

Winged Victory: from Wikipedia

Let’s take this as an opportunity to describe what a win vector is. Continue reading What is a win vector?