Slides from the PyData2019 data_algebra lightning talk
Slides from my PyData2019 data_algebra lightning talk are here. [...Read more...]
Python-bloggers
Data science news and tutorials - contributed by Python bloggers
Articles by John Mount
New Introduction to the data_algebra
We’ve had really good progress in bringing the Python data_algebra to feature parity with R rquery. In fact we are able to reproduced the New Introduction to rquery article as a “New Introduction to the data_algebra” here. The idea is: you may have good reasons to want ... [...Read more...]
Free R/datascience Extract: Evaluating a Classification Model with a Spam Filter
We are excited to share a free extract of Zumel, Mount, Practical Data Science with R, 2nd Edition, Manning 2019: Evaluating a Classification Model with a Spam Filter. This section reflects an important design decision in the book: teach model evaluation first, and as a step separate from model construction. It ...
AI for Engineers
For the last year we (Nina Zumel, and myself: John Mount) have had the honor of teaching the AI200 portion of LinkedIn’s AI Academy. John Mount at the LinkedIn campus Nina Zumel designed most of the material, and John Mount has been delivering it and bringing her feedback. We’...
vtreat Cross Validation
Nina Zumel finished new documentation on how vtreat‘s cross validation works, which I want to share here. vtreat is a system that makes data preparation for machine learning a “one-liner” (available in R or available in Python). We have a set of starting off points here. These documents describe ... [...Read more...]
New vtreat Documentation (Starting with Multinomial Classification)
Nina Zumel finished some great new documentation showing how to use Python vtreat to prepare data for multinomial classification mode. And I have finally finished porting the documentation to R vtreat. So we now have good introductions on how to use vtreat to prepare data for the common tasks of: ... [...Read more...]
How to Prepare Data
Real world data can present a number of challenges to data science workflows. Even properly structured data (each interesting measurement already landed in distinct columns), can present problems, such as missing values and high cardinality categorical variables. In this note we describe some great tools for working with such data. ... [...Read more...]
Preparing Data for Supervised Classification
Nina Zumel has been polishing up new vtreat for Python documentation and tutorials. They are coming out so good that I find to be fair to the R community I must start to back-port this new documentation to vtreat for R. vtreat is a package for systematically preparing data for ... [...Read more...]
The Advantages of Record Transform Specifications
Nina Zumel had a really great article on how to prepare a nice Keras performance plot using R. I will use this example to show some of the advantages of cdata record transform specifications. The model performance data from Keras is in the following...
Advanced Data Reshaping in Python and R
This note is a simple data wrangling example worked using both the Python data_algebra package and the R cdata package. Both of these packages make data wrangling easy through he use of coordinatized data concepts (relying heavily on Codd’s “rule of access”). The advantages of data_algebra and ... [...Read more...]
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