I demonstrate a Kelly/Thorp betting system for the simple card game of guessing if the next card from a standard deck is red or black. I have a video of the play here. And a derivation of the betting strategy in R is here. A derivation of the proof you […]
In R it has always been incorrect to call order() on a data.frame. Such a call doesn’t return a sort-order of the rows, and previously did not return an error. For example. d <- data.frame( x = c(2, 2, 3, 3, 1, 1), y = 6:1) knitr::kable(d) x y 2 […]
The wrapr R package supplies a number of substantial programming tools, including the S3/S4 compatible dot-pipe, unpack/pack object tools, and many more. It also supplies a number of formatting and parsing convenience tools: qc() (“quoting concatenate”): quotes strings, giving value-oriented interfaces much of the incidental convenience of non-standard evaluation (NSE) […]
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Introduction Teaching basic data science, machine learning, and statistics is great due to the questions. Students ask brilliant questions, as they see what holes are present in your presentation and scaffolding. The students are not yet conditioned to ask only what you feel is easy to answer or present. They […]
I’d like to share a new talk on bilingual data science. It is limited to R and Python, so it is a bit of a “we play all kinds of music, both Country and Western.” It has what I feel is a really neat example how I used Jetbrains Intellij […]
I’ve now shared the code for my “Variable Utility is not Intrinsic” article here: https://github.com/WinVector/Examples/tree/main/Variable_Utility_is_not_Intrinsic. And I have also ported the entire article to Python. It is actually kind of neat to be able to compare the two and see how close doing data science in R and in Python […]
There is much ado about variable selection or variable utility valuation in supervised machine learning. In this note we will try to disarm some possibly common fallacies, and to set reasonable expectations about how variable valuation can work. Introduction In general variable valuation is estimating the utility that a column […]
Introduction Here is a quick data-scientist / data-analyst question: what is the overall trend or shape in the following noisy data? For our specific example: How do we relate value as a noisy function (or relation) of m? This example arose in producing our tutorial “The Nature of Overfitting”. One […]
Introduction I would like to talk about the nature of supervised machine learning and overfitting. One of the cornerstones of our data science intensives is giving the participants the experiences of a data scientist in a safe controlled environment. We hope by working examples they can quickly get to the […]
