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Category: Data Science

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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Options with stats::density() in R

Published 2019-08-07 by Kevin Feasel

Evgeni Chasnovski takes us through what the parameters in the stats::density() R function do:

Argument bw is responsible for computing bandwith of kernel density estimation: one of the main parameters that greatly affect the output. It can be specified as either algorithm of computation or directly as number. Because actual bandwidth is computed as adjust*bw(adjust is another density() argument, which is explored in the next section), here we will see how different algorithms compute bandwidths, and the effect of changing numeric value of bandwidth will be shown in section about adjust.

There are 5 available algorithms: “nrd0”, “nrd”, “ucv”, “bcv”, “SJ”. 

Evgeni has also created animations for each of these, so it’s easy to see what they do compared to the default output.

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Naive Bayes Predictions with Analysis Services

Published 2019-08-05 by Kevin Feasel

Dinesh Asanka shows how you can use the Naive Bayes algorithm in an Analysis Services data mining project:

Microsoft Naive Bayes is a classification supervised learning. This data set can be bi-class which means it has only two classes. Whether the patient is suffering from dengue or not or whether your customers are bike buyers or not, are an example of the bi-class data set. There can be multi-class data set as well.

Let us take the example which we discussed in the previous article, AdventureWorks bike buyer example. In this example, we will use vTargetMail database view in the AdventureWorksDW database.

During the data mining algorithm wizard, the Microsoft Naive Bayes algorithm should be selected as shown in the below image.

Of mild interest is that it’s a two-class classifier here, but it’s a multi-class classifier in the (much) later ML.NET.

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Validating Errors in A/B Testing

Published 2019-08-02 by Kevin Feasel

Roland Stevenson shows us how to validate Type I and Type II errors when performing A/B tests in R:

In this post, we seek to develop an intuitive sense of what type I (false-positive) and type II (false-negative) errors represent when comparing metrics in A/B tests, in order to gain an appreciation for “peeking”, one of the major problems plaguing the analysis of A/B test today.

To better understand what “peeking” is, it helps to first understand how to properly run a test. We will focus on the case of testing whether there is a difference between the conversion rates cr_a and cr_b for groups A and B. We define conversion rate as the total number of conversions in a group divided by the total number of subjects. The basic idea is that we create two experiences, A and B, and give half of the randomly-selected subjects experience A and half B. Then, after some number of users have gone through our test, we measure how many conversions happened in each group. The important question is: how many users do we need to have in groups A and B in order to measure a difference in conversion rates of a particular size?

Read the whole thing. H/T R-Bloggers

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Explaining Tree-Based Algorithms

Published 2019-07-31 by Kevin Feasel

Stephanie Glen takes us through quick explanations of decision trees, random forests, and gradient boosting:

The three methods are similar, with a significant amount of overlap. In a nutshell:

– A decision tree is a simple, decision making-diagram.
– Random forests are a large number of trees, combined (using averages or “majority rules”) at the end of the process.
– Gradient boosting machines also combine decision trees, but start the combining process at the beginning, instead of at the end.

Read on for more details. All three are useful algorithms serving similar but slightly different purposes.

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Nowcasting Unemployment

Published 2019-07-30 by Kevin Feasel

Peter Ellis takes us through an attempt to perform near-term projection of Australian unemployment rates based on macroeconomic indicators:

“Leading” in this case will have to mean pretty fast, because the official unemployment stats in Australia come out from the Australian Bureau of Statistics (ABS) with admirable promptitude given the complexity of managing the Labour Force Survey. ABS Series 6202.0 – the monthly summary from the Labour Force Survey – comes out around two weeks after the reference month. Only a few economic variables of interest are available faster than that. In this blog post I look at two candidates for leading information that are readily available in more or less real time – interest rates and stock exchange prices.

One big change in the past decade in this sort of short-term forecasting of unemployment has been to model the transitions between participation, employed and unemployed people, rather than direct modelling of the resulting proportions. This innovation comes from an interesting 2012 paper by Barnichon and Nekarda. I’ve only skimmed this paper, but I’d like to look into how much of the gains they report comes from the focus on workforce transitions, and how much from their inclusion of new information in the form of vacancy postings and claims for unemployment insurance. My suspicion is that these latter two series have powerful new information. I will certainly be returning to vacancy information and job adverts at a later time – these are items which feature prominently for me in my day job at Nous Group in analysing the labour market.

This gets a little deep but it’s well worth the read. H/T R-bloggers

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