Category: Data Science

Nina Zumel compares a pair of methods for performing regression when income is the dependent variable:

Regressing against the log of the outcome will not be calibrated; however it has the advantage that the resulting model will have lower relative error than a Poisson regression against income. Minimizing relative error is appropriate in situations when differences are naturally expressed in percentages rather than in absolute amounts. Again, this is common when financial data is involved: raises in salary tend to be in terms of percentage of income, not in absolute dollar increments.

Unfortunately, a full discussion of the differences between Poisson regression and regressing against log amounts was outside of the scope of our book, so we will discuss it in this note.

This is an interesting post with a great teaser for the next post in the series.

Julia Silge announces a new package, tidylo:

The package contains examples in the README and vignette, but let’s walk though another, different example here. This weighted log odds approach is useful for text analysis, but not only for text analysis. In the weeks since we’ve had this package up and running, I’ve found myself reaching for it in multiple situations, both text and not, in my real-life day job. For this example, let’s look at the same data as my last post, names given to children in the US.

Which names were most common in the 1950s, 1960s, 1970s, and 1980?

This package looks like it’s worth checking out if you deal with frequency-based problems.

Neil Saunders takes us through an interesting problem:

A recent question on Stack Overflow [r] asked why a random forest model was not working as expected. The questioner was working with data from an experiment in which yeast was grown under conditions where (a) the growth rate could be controlled and (b) one of 6 nutrients was limited. Their dataset consisted of 6 rows – one per nutrient – and several thousand columns, with values representing the activity (expression) of yeast genes. Could the expression values be used to predict the limiting nutrient?

The random forest was not working as expected: not one of the nutrients was correctly classified. I pointed out that with only one case for each outcome, this was to be expected – as the random forest algorithm samples a proportion of the rows, no correct predictions are likely in this case. As sometimes happens the question was promptly deleted, which was unfortunate as we could have further explored the problem.

Neil decided to explore the problem further regardless and came to some interesting conclusions.

Nina Zumel takes us through Cohen’s D, a useful tool for determining effect sizes in experiments:

Cohen’s d is a measure of effect size for the difference of two means that takes the variance of the population into account. It’s defined as
d = | μ₁ – μ₂ | / σ_pooled
where σ_pooled is the pooled standard deviation over both cohorts.

Read the whole thing.

Bruno Rodrigues shows one technique for forecasting intermittent data:

Now, it is clear that this will be tricky to forecast. There is no discernible pattern, no trend, no seasonality… nothing that would make it “easy” for a model to learn how to forecast such data.

This is typical intermittent demand data. Specific methods have been developed to forecast such data, the most well-known being Croston, as detailed in this paper. A function to estimate such models is available in the {tsintermittent} package, written by Nikolaos Kourentzes who also wrote another package, {nnfor}, which uses Neural Networks to forecast time series data. I am going to use both to try to forecast the intermittent demand for the {RDieHarder} package for the year 2019.

Read the whole thing. H/T R-Bloggers