Practical Data Science with R, 2nd Edition author Dr. Nina Zumel, with a fresh author’s copy of her book!
Nina Zumel has been polishing up new
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
I will use this example to show some of the advantages of
cdata record transform specifications.
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 an appropriate amount of challenge is required for significant learning and accomplishment.
The second edition is coming out very soon. Please check it out.
data_algebraproject: a data processing tool family available in
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 (
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 explanatory variables (typically numeric or categorical/string-valued, these columns may have missing values) that the user later wants to use to predict “y”. In practice such an input
DataFrame may not be immediately suitable for machine learning procedures that often expect only numeric explanatory variables, and may not tolerate missing values.
To solve this,
vtreat builds a transformed
DataFrame where all explanatory variable columns have been transformed into a number of numeric explanatory variable columns, without missing values. The
vtreat implementation produces derived numeric columns that capture most of the information relating the explanatory columns to the specified “y” or dependent/outcome column through a number of numeric transforms (indicator variables, impact codes, prevalence codes, and more). This transformed
DataFrame is suitable for a wide range of supervised learning methods from linear regression, through gradient boosted machines.
The idea is: you can take a
DataFrame of messy real world data and easily, faithfully, reliably, and repeatably prepare it for machine learning using documented methods using
vtreat into your machine learning workflow lets you quickly work with very diverse structured data.
Worked examples can be found here.
(logo: Julie Mount, source: “The Harvest” by Boris Kustodiev 1914)
Some operational examples can be found here.
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 Science using R. We have been working hard on an improved and revised 2nd edition of our book (coming out this Fall). The book reflects more experience with data science, teaching, and with R itself. We will talk about what direction we think the R community has been taking, how this affected the book, and what is new in the upcoming edition.
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 least try one of the open-source or the trial versions.