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 is funny, but it … Continue reading Free R/datascience Extract: Evaluating a Classification Model with a Spam Filter
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’ve just started our 9th cohort. We … Continue reading AI for Engineers
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 what vtreat does for you, you … Continue reading vtreat Cross Validation
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: Regression: R regression example, Python … Continue reading New vtreat Documentation (Starting with Multinomial Classification)
When studying regression models, One of the first diagnostic plots most students learn is to plot residuals versus the model’s predictions (that is, with the predictions on the x-axis). Here’s a basic example. # build an “ideal” linear process. set.seed(34524) N = 100 x1 = runif(N) x2 = runif(N) noise = 0.25*rnorm(N) y = x1 … Continue reading Why Do We Plot Predictions on the x-axis?
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. For an example: consider the … Continue reading How to Prepare Data
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 supervised machine learning tasks such … Continue reading Preparing Data for Supervised Classification
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…
Just got the following note from a new reader: Thank you for writing Practical Data Science with R. It’s challenging for me, but I am learning a lot by following your steps and entering the commands. Wow, this is exactly what Nina Zumel and I hoped for. We wish we could make everything easy, but … Continue reading Practical Data Science with R update
I have put a new release of the WVPlots package up on CRAN. This release adds palette and/or color controls to most of the plotting functions in the package. WVPlots was originally a catch-all package of ggplot2 visualizations that we at Win-Vector tended to use repeatedly, and wanted to turn into “one-liners.” A consequence of … Continue reading WVPlots 1.1.2 on CRAN
This article introduces the data_algebra project: a data processing tool family available in R and Python. These tools are designed to transform data either in-memory or on remote databases. In particular we will discuss the Python implementation (also called data_algebra) and its relation to the mature R implementations (rquery and rqdatatable). Introduction Parts of the … Continue reading Introducing data_algebra
vtreat is a DataFrame processor/conditioner that prepares real-world data for supervised machine learning or predictive modeling in a statistically sound manner. vtreat takes an input DataFrame that has a specified column called “the outcome variable” (or “y”) that is the quantity to be predicted (and must not have missing values). Other input columns are possible … Continue reading What is vtreat?
We will be speaking at the Tuesday, September 3, 2019 BARUG. If you are in the Bay Area, please come see us. Nina Zumel & John Mount Practical Data Science with R Practical Data Science with R (Zumel and Mount) was one of the first, and most widely-read books on the practice of doing Data … Continue reading Speaking at BARUG
Florence Nightingale, Data Scientist.
In 1858 Florence Nightingale published her now famous “rose diagram” breaking down causes of mortality.
By w:Florence Nightingale (1820–1910). – http://www.royal.gov.uk/output/Page3943.asp [dea…
In 1876 A. Légé & Co., 20 Cross Street, Hatton Gardens, London completed the first “tide calculating machine” for William Thomson (later Lord Kelvin) (ref). Thomson’s (Lord Kelvin) First Tide Predicting Machine, 1876 The results were plotted on the paper cylinders, and one literally “turned the crank” to perform the calculations. The tide calculating machine … Continue reading Lord Kelvin, Data Scientist
My basic video review of the PyCharm integrated development environment for Python with Anaconda and Jupyter/iPython integration. I like the IDE extensions enough to pay for them early in my evaluation. Highly recommended for data science projects, at…
A point that differs from our experience struck us in the recent note: A development environment specifically tailored to the data science sector on the level of RStudio, for example, does not (yet) exist. “What’s the Best Statistical Software? A Comparison of R, Python, SAS, SPSS and STATA” Amit Ghosh Actually, Python has a large … Continue reading A Comment on Data Science Integrated Development Environments
In the previous article in this series, we showed that common ensemble models like random forest and gradient boosting are uncalibrated: they are not guaranteed to estimate aggregates or rollups of the data in an unbiased way. However, they can be preferable to calibrated models such as linear or generalized linear regression, when they make … Continue reading An Ad-hoc Method for Calibrating Uncalibrated Models
by Bob Muenchen & Sean Mackinnon One of us (Muenchen) has been tracking The Popularity of Data Science Software using a variety of different approaches. One approach is to use Google Scholar to count the number of scholarly articles found … Continue reading →
While reading Dr. Nina Zumel’s excellent note on bias in common ensemble methods, I ran the examples to see the effects she described (and I think it is very important that she is establishing the issue, prior to discussing mitigation). In doing that I ran into one more avoidable but strange issue in using xgboost: when … Continue reading Some Details on Running xgboost