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Category: Data Science

Market Basket Analysis With arulesSequences

Published 2019-02-04 by Kevin Feasel

Allison Koenecke takes us through the arulesSequences package in R:

In the following tutorial, we answer both questions using the R package arulesSequences [4], which implements the SPADE algorithm [5]. Concretely, given data in an Excel spreadsheet containing historical customer service purchase data, we produce two separate Excel sheet deliverables: a list of service bundles, and a set of temporal rules showing how service bundles evolve over time.  We will focus on interpreting the latter result by showing how to use temporal rules in making predictive sales recommendations.

Our running example below is inspired by the need for Microsoft’s Azure Services salespeople to suggest which additional products to recommend to customers, given the customers’ current cloud product consumption services mix.  We’d like to know, for instance, if customers who have implemented web services also purchase web analytics within the next month.  Actual Azure Service names have been removed for confidentiality reasons.

Market basket analysis is an interesting topic, though in my limited experience, it really falls apart when you have a large number of products to compare, so it tends to work better with toy examples or limited product selections because when you have a 50,000+ SKU inventory, the lift of any individual combination of products rarely gets above the level of noise.

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Building Credit Scorecards

Published 2019-01-25 by Kevin Feasel

Andre Violante uses SAS to build credit scorecards and analyze credit data:

For this analysis I’m using the SAS Open Source library called SWAT (Scripting Wrapper for Analytics Transfer) to code in Python and execute SAS CAS Action Sets. SWAT acts as a bridge between the python language to CAS Action Sets. CAS Action Sets are synonymous to libraries in Python or packages in R. The one main difference and benefit is that the algorithms within these action sets have been highly parallelized to run on a CAS (Cloud Analytic Services) server. The CAS server is a distributed in-memory engine where I can do all my heavy lifting or computations. The code and Jupyter Notebook are available on GitHub.

Click through for the analysis.

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P-Hacking and Multiple Comparison Bias

Published 2019-01-21 by Kevin Feasel

Patrick David has a great article on hypothesis testing, p-hacking, and multiple comparison bias:

The most important part of hypothesis testing is being clear what question we are trying to answer. In our case we are asking:
“Could the most extreme value happen by chance?”
The most extreme value we define as the greatest absolute AMVR deviation from the mean. This question forms our null hypothesis.

Give this one a careful read and try out the code. This is an important topic for anyone who analyzes data to understand.

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Feature And Text Classification Using Naive Bayes In R

Published 2019-01-18 by Kevin Feasel

I wrap up my series on the Naive Bayes class of algorithms, finally writing some code along the way:

Now we’re going to look at movie reviews and predict whether a movie review is a positive or a negative review based on its words. If you want to play along at home, grab the data set, which is under 3MB zipped in 2000 reviews in total.

Unike last time, I’m going to break this out into sections with commentary in between. If you want the full script with notebook, check out the GitHub repo I put together for this talk.

Assuming I ever get a chance to do this talk again, I’m probably going to change the data sets in the example given how overplayed iris is.

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Classifying Texts With Naive Bayes

Published 2019-01-17 by Kevin Feasel

I continue my series on Naive Bayes with another hand-calculation post:

Step two is, on the surface, pretty tough: how do we figure out if a set of words is a business phrase or a baseball phrase? We could try to think up a set of features. For example, how long is the phrase? How many unique words does it have? Is there a pile of sunflower seeds near the phrase? But there’s an easier way.

Remember the “naive” part of Naive Bayes: all features are independent. And in this case, we can use as features the individual words. Therefore, the probability of a word being a baseball-related word or a business-related word is what matters, and we cross-multiply those probabilities to determine if the overall phrase is a baseball phrase or a business phrase.

Click through for a sports-heavy example and a bonus Nate Barkerson reference.

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Where Machine Learning And Econometrics Collide

Published 2019-01-16 by Kevin Feasel

Dave Giles shares some thoughts on how machine learning and econometrics relate:

What is Machine Learning (ML), and how does it differ from Statistics (and hence, implicitly, from Econometrics)?

Those are big questions, but I think that they’re ones that econometricians should be thinking about. And if I were starting out in Econometrics today, I’d take a long, hard look at what’s going on in ML.

Click through for some quick thoughts and several resources on the topic.

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Solving Naive Bayes By Hand

Published 2019-01-11 by Kevin Feasel

I have a post that requires math and is meaner toward the Buffalo Bills than I normally am:

Trust the Process
There are three steps to the process of solving the simplest of Naive Bayes algorithms. They are:
1. Find the probability of winning a game (that is, our prior probability).
2. Find the probability of winning given each input variable: whether Josh Allen starts the game, whether the team is home or away, whether the team scores 14 points, and who the top receiver was.
3. Plug in values from our new data into the formula to obtain the posterior probability.

This is an algorithm you want to solve by hand first—it’s just that easy. Then, once you understand it, let a computer do the work for larger data sets. Also, Super Bowl 2020 because I’m the kind of overly optimistic fool required of Bills fans. Just gonna leave this link here.

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The Basics Of Naive Bayes Classifiers

Published 2019-01-09 by Kevin Feasel

I have the first post in a series up on using the Naive Bayes class of algorithms for classifying inputs:

Why Should We Use Naive Bayes? Is It the Best Classifier Out There?
Probably not, no. In fact, it’s typically a mediocre classifier—it’s the one you strive to beat with your fancy algorithm. So why even care about this one?
Because it’s fast, easy to understand, and it works reasonably well. In other words, this is the classifier you start with to figure out if it’s worth investing your time on a problem. If you need to hit 90% category accuracy and Naive Bayes is giving you 70%, you’re probably in good shape; if it’s giving you 20% accuracy, you might need to take another look at whether you have a viable solution given your data.

Click through to learn what day it is based on what some fictional fellow has as head covering. Also, learn what it is I actually mean when I let “update your priors” slip.

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Practical AI Workshop Notebooks

Published 2019-01-04 by Kevin Feasel

David Smith has published a set of notebooks from the Practical AI for the Working Software Engineer workshop:

Last month, I delivered the one-day workshop Practical AI for the Working Software Engineer at the Artificial Intelligence Live conference in Orlando. As the title suggests, the workshop was aimed at developers, bu I didn’t assume any particular programming language background. In addition to the lecture slides, the workshop was delivered as a series of Jupyter notebooks. I ran them using Azure Notebooks (which meant the participants had nothing to install and very little to set up), but you can run them in any Jupyter environment you like, as long as it has access to R and Python. You can download the notebooks and slides from this Github repository (and feedback is welcome there, too). 

Read on for details about those notebooks and to get your own copies.

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Dynamic Programming In R With RCppDynProg

Published 2019-01-03 by Kevin Feasel

John Mount has a new package available in R:

In the above we have an input (or independent variable) x and an observed outcome (or dependent variable) y_observed (portrayed as points). y_observed is the unobserved idea value y_ideal (portrayed by the dashed curve) plus independent noise. The modeling goal is to get close the y_ideal curve using the y_observed observations. Obviously this can be done with a smoothing spline, but let’s use RcppDynProg to find a piecewise linear fit.
To encode this as a dynamic programming problem we need to build a cost matrix that for every consecutive interval of x-values we have estimated the out-of sample quality of fit. This is supplied by the function RcppDynProg::lin_costs() (using the PRESS statistic), but lets take a quick look at the idea.

It’s an interesting package whose purpose is to turn an input data stream into a set of linear functions which approximate the stream. I’m not sure I’ll ever have a chance to use it, but it’s good to know that it’s there if I do ever need it.

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