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Category: Data Science

Python and R Data Reshaping

Published 2019-09-10 by Kevin Feasel

John Mount takes us through a couple of data shaping packages:

The advantages of data_algebra and cdata are:

– The user specifies their desired transform declaratively by example and in data. What one does is: work an example, and then write down what you want (we have a tutorial on this here).
– The transform systems can print what a transform is going to do. This makes reasoning about data transforms much easier.
– The transforms, as they themselves are written as data, can be easily shared between systems (such as R and Python).

Let’s re-work a small R cdata example, using the Python package data_algebra.

Click through for the example.

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When to Use Different ML Algorithms

Published 2019-09-09 by Kevin Feasel

Stefan Franczuk explains the different categories of machine learning algorithms available in Talend:

Clustering is the task of grouping together a set of objects in such a way, that objects in the same group are more similar to each other than to those in other groups. Clustering is really useful for identify separate groups and therefore is used to solve use cases such as “who are my premium customers?”.

Understanding when to use which algorithm is important. You don’t want to build out the world’s best regression if your benefactors are asking for a classifier.

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Exploratory Data Analysis with ExPanDaR

Published 2019-09-04 by Kevin Feasel

Joachim Gassen walks us through the ExPanDaR package in R:

The ‘ExPanDaR’ package offers a toolbox for interactive exploratory data analysis (EDA). You can read more about it here. The ‘ExPanD’ shiny app allows you to customize your analysis to some extent but often you might want to continue and extend your analysis with additional models and visualizations that are not part of the ‘ExPanDaR’ package.

Thus, I am currently developing an option to export the ‘ExPanD’ data and analysis to an R Notebook. While it is not ready for CRAN yet, it seems to work reasonably well and I would love to see some people trying it and letting me know about any bugs or other issues that they encounter. Hence, this blog post.

Looks like an interesting package. H/T R-bloggers

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Calculating Consistency of Ratings

Published 2019-09-03 by Kevin Feasel

Sebastian Sauer looks at computing reliability between raters:

Computing inter-rater reliability is a well-known, albeit maybe not very frequent task in data analysis. If there’s only one criteria and two raters, the proceeding is straigt forward; Cohen’s Kappa is the most widely used coefficient for that purpose. It is more challenging to compare multiple raters on one criterion; Fleiss’ Kappa is one way to get a coefficient. If there are multiple criteria, one way is to compute the mean of multiple Fleiss’ coefficients.

However, a different way, and the way presented in this post, consists of checking of all raters agree on one given item (and repeating that for all items). If rater A assigns two tags/criteria (tag1, tag2) to item A, then the other raters may not assign different tags (eg tag3, tag4) to that item, if a match should be scored. Note that this proceeding allows for different numbers of tags/criteria for the items (eg., item 1 with only 1 tag, but item 2 with 3 tags etc.). However, our grading should give some points, if, say, rater1 assigns tag1 and tag2, but raters 2 and 3 only assign tag1.

Read the whole thing.

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Sampling and Estimating Rare Events

Published 2019-08-28 by Kevin Feasel

Yi Liu takes us through a process to estimate rare events:

Naturally, we get an unbiased estimate of the overall prevalence of violation if we sample the videos uniformly from the population and have them reviewed by human raters to estimate the proportion of violating videos. We also get an unbiased estimate of the violation rate in each policy vertical. But given the low probability of violation and wanting to use our rater capacity wisely, this is not an adequate solution — we typically have too few positive labels in uniform samples to achieve an accurate estimate of the prevalence, especially for those sensitive policy verticals. To obtain a relative error of no more than 20%, we need roughly 100 positive labels, and more often than not, we have zero violation videos in the uniform samples for rarer policies.

This is similar in nature to testing for rare diseases, where a random sample of N people in the population is likely to turn up 0 cases of it.

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MAPE and Its Flaws

Published 2019-08-22 by Kevin Feasel

Jan Fischer takes us through Mean Absolute Percentage Error as a measure of forecast quality:

Particular small actual values bias the MAPE.
If any true values are very close to zero, the corresponding absolute percentage errors will be extremely high and therefore bias the informativity of the MAPE (Hyndman & Koehler 2006). The following graph clarifies this point. Although all three forecasts have the same absolute errors, the MAPE of the time series with only one extremely small value is approximately twice as high as the MAPE of the other forecasts. This issue implies that the MAPE should be used carefully if there are extremely small observations and directly motivates the last and often ignored the weakness of the MAPE.

Jan also points out a couple of things people criticize MAPE for incorrectly, but several things for which it is actually guilty. It’s not a bad measure if you can make certain data assumptions, but Jan has a few alternatives which tend to be better than MAPE.

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Calculating AUC in R

Published 2019-08-20 by Kevin Feasel

Andrew Treadway shows how you can calculate Area Under the Curve in R:

AUC is an important metric in machine learning for classification. It is often used as a measure of a model’s performance. In effect, AUC is a measure between 0 and 1 of a model’s performance that rank-orders predictions from a model. For a detailed explanation of AUC, see this link.

Since AUC is widely used, being able to get a confidence interval around this metric is valuable to both better demonstrate a model’s performance, as well as to better compare two or more models. For example, if model A has an AUC higher than model B, but the 95% confidence interval around each AUC value overlaps, then the models may not be statistically different in performance. We can get a confidence interval around AUC using R’s pROC package, which uses bootstrapping to calculate the interval.

There are plenty of ways to calculate this useful metric, but this is definitely one of the easier methods. H/T R-bloggers

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Python versus R (Again)

Published 2019-08-16 by Kevin Feasel

Alex Woodie looks at whether Python is dominating R in the data science space:

There is some evidence that Python’s popularity is hurting R usage. According to the TIOBE Index, Python is currently the third most popular language in the world, behind perennial heavyweights Java and C. From August 2018 to August 2019, Python usage surged by more than 3% to achieve a 10% rating (TIOBE’s proprietary metric that primarily measures search activity), easily the biggest gain among the 20 most popular languages.

R, by contrast, has not fared well lately on the TIOBE Index, where it dropped from 8th place in January 2018 to become the 20th most popular language today, behind Perl, Swift, and Go. At its peak in January 2018, R had a popularity rating of about 2.6%. But today it’s down to 0.8%, according to the TIOBE index.

I’ll say that rumors of R’s demise are premature.

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Contrasting Logistic Regression and Decision Trees

Published 2019-08-12 by Kevin Feasel

Shital Katkar explains cases when you might use logistic regression or decision trees for classification problems:

Categorical data works well with Decision Trees, while continuous data work well with Logistic Regression.

If your data is categorical, then Logistic Regression cannot handle pure categorical data (string format). Rather, you need to convert it into numerical data.

Each algorithm has its own uses and assumptions.

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