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Category: R

Interpreting Linear Models with SHAP

Published 2022-12-27 by Kevin Feasel

Michael Mayer answers a question:

XGBoost models are often interpreted with SHAP (Shapley Additive eXplanations): Each of e.g. 1000 randomly selected predictions is fairly decomposed into contributions of the features using the extremely fast TreeSHAP algorithm, providing a rich interpretation of the model as a whole. TreeSHAP was introduced in the Nature publication by Lundberg and Lee (2020).

Can we do the same for non-tree-based models like a complex GLM or a neural network? Yes, but we have to resort to slower model-agnostic SHAP algorithms:

Read on for examples of those algorithms and an example of interpretation and analysis.

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Working with R in AML v2

Published 2022-12-23 by Kevin Feasel

Tomaz Kastrun ends the advent of Azure ML on a downer:

R language and Azure Machine Learning SDK for R was deprecated a year ago (end of 2021). But R can be still used for training and deployment by using Azure Machine learning CLI 2.0!

Furthermore, R language can be used in Machine Learning Designer, for data preparation, data wrangling and statistical analysis.

You can work with R but they make sure everything is more difficult.

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Coloring Tree Branches in R

Published 2022-12-20 by Kevin Feasel

Ali Oghabian marks up a tree:

After running Hierarchical clustering we can cut the result binary tree at a certain depth or request that it be cut in a manner that would result a certain number of clusters. Here, I request that the resulted binary tree be cut in away that would result to 2 sample clusters. Furthermore, I convert the resulted tree to a “dendogram” object and colour the branches and the labels of the tree to visualize the 2 clusters. One can use color_branches and color_labels functions to cut and colour the trees.

Read on for a demonstration. H/T R-Bloggers.

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An Overview of R7

Published 2022-12-19 by Kevin Feasel

Nicola Rennie explains the purpose of a new standard for object-oriented programming in R:

The two main OOP systems in R, S3 and S4, both have their advantages and their limitations. For example, in S3 there’s no systematic object validation to make sure an object’s class is correct. In S4, the syntax for defining classes is rather unusual and relies on side effects. Issues such as these mean that, unlike other programming languages, there isn’t a dominant approach to OOP in R.

Now imagine you could take the best bits of S3 and the best bits of S4. That’s where R7 comes in. 

Read on to learn more about how R7 compares to other object-oriented paradigms in R, such as S3, S4, and R6.

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Running Python Code from R via Reticulate

Published 2022-12-12 by Kevin Feasel

Rick Pack crosses the streams:

I wanted a REPL (read-evaluate-print-loop) so that I could quickly experiment with Python without, for the moment, leaping over what some consider one of the biggest hurdles to Python usage: Work environment set up.

The reticulate R package by Posit enables the use of Python while working within the R Studio IDE. One can find a Posit tutorial here.

Read on for Rick’s notes.

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An Overview of Quarto for R Users

Published 2022-12-09 by Kevin Feasel

Nicola Rennie and Colin Gillespie provide an overview of Quarto:

Earlier this year, Posit (formerly RStudio) released Quarto. Quarto is an open-source scientific and technical publishing system that allows you to weave together narrative text and code to produce high-quality outputs including reports, presentations, websites, and more.

One of the main features of Quarto is that it isn’t just built for R. It’s language-agnostic. It can render documents that contain code written in R, Python, Julia, or Observable. That makes it incredibly useful if you work in multilingual teams, or collaborate with people who write in a different programming language from you. But what if you don’t use any other programming languages? What benefits does Quarto bring to people who only use R?

Read on to learn why you might want to use it over R Markdown.

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Installing ML Services on SQL Server 2022

Published 2022-12-08 by Kevin Feasel

Tomaz Kastrun notices a change to the SQL Server installer:

Machine Learning Services and language extensions is available under Database Engine Services, and if you want to use any of these languages, check this feature. During the installation process, the R, Python or Java will not be installed (nor asked for permissions), but you will install your own runtime after the installation. This will bring you more convenience with the installation of different R/Python/Java runtimes.

Read on to see how you can install and work with languages like R, Python, and Java in SQL Server 2022.

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Connecting to SQL Server from R

Published 2022-12-06 by Kevin Feasel

Thomas Williams makes a database call:

R code (including in R Markdown and Shiny) can connect to databases, for both reading and writing. This opens up a whole world of powerful analysis – however, it can sometimes be tricky to deal with drivers, error handling and closing connections. In this post, I’ll demonstrate how to connect to a SQL Server database and run a query; in part 2, I’ll cover some of the things you might want to do with the query results.

Click through for two common libraries and a few tips.

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Fun with Decision Trees

Published 2022-11-28 by Kevin Feasel

Holger von Jouanne-Diedrich explains the value of decision trees, using predictive maintenance as an example:

Predictive Maintenance is one of the big revolutions happening across all major industries right now. Instead of changing parts regularly or even only after they failed it uses Machine Learning methods to predict when a part is going to fail.

If you want to get an introduction to this fascinating developing area, read on!

Click through for an example of how it works.

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Making a Newsletter Template in R

Published 2022-11-22 by Kevin Feasel

Benjamin Smith’s ideas are intriguing to me and I wish to subscribe to his newsletter:

Jinja is a powerful templating engine that is useful in a variety of contexts. Recently, I discovered how its possible to use the power of Jinja syntax in R with the jinjar package written by David C Hall. With jinjar and the tidyRSS package by Robert Myles it is possible to make an email template that can provide short and informative updates. In his blog, I’m going to share how the jinjar and tidyRSS packages work and show how to combine them to make a simple daily email newsletter.

Read on to learn how.

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