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Category: Data Science

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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An Intro to k-Means Clustering

Published 2019-07-30 by Kevin Feasel

Holger von Jouanne-Diedrich takes us through an example of how k-means clustering works:

The guiding principles are:

– The distance between data points within clusters should be as small as possible.
– The distance of the centroids (= centres of the clusters) should be as big as possible.

Because there are too many possible combinations of all possible clusters comprising all possible data points k-means follows an iterative approach

Click through for a demonstration. I appreciate adding visualizations for intermediate steps in there as well because it gives you an intuitive understanding for what the one-liner function is really doing.

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IDEs and Cloudera Data Science Workbench

Published 2019-07-25 by Kevin Feasel

Bethann Noble walks us through some of the options available for IDEs operating against Cloudera Data Science Workbench:

Other coders on the team including ML and DevOps engineers often work in local IDEs such as PyCharm.  These applications run locally on the user’s computer and connect to CDSW remotely over SSH for code completion and execution.  They must be configured per user and are not associated at the project level in CDSW. The documentation provides sample instructions for the Professional Edition of PyCharm v2019.1.

They support both browser-based and local IDEs.

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Polishing Uncalibrated Models

Published 2019-07-23 by Kevin Feasel

Nina Zumel takes an uncalibrated random forest model and applies a calibration technique to improve the estimate on one variable:

In the previous article in this series, we showed that common ensemble models like random forest and gradient boosting are uncalibrated: they are not guaranteed to estimate aggregates or rollups of the data in an unbiased way. However, they can be preferable to calibrated models such as linear or generalized linear regression, when they make more accurate predictions on individuals. In this article, we’ll demonstrate one ad-hoc method for calibrating an uncalibrated model with respect to specific grouping variables. This “polishing step” potentially returns a model that estimates certain rollups in an unbiased way, while retaining good performance on individual predictions.

This is a great explanation of the process as well as its risks and limitations.

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Comparing Classification Model Quality

Published 2019-07-22 by Kevin Feasel

Stephanie Glen looks at ways to compare model evaluation for classification models:

In part 1, I compared a few model evaluation techniques that fall under the umbrella of ‘general statistical tools and tests’. Here in Part 2 I compare three of the more popular model evaluation techniques for classification and clustering: confusion matrix, gain and lift chart, and ROC curve. The main difference between the three techniques is that each focuses on a different type of result:

– Confusion matrix: false positives, false negatives, true positives and true negatives.
– Gain and lift: focus is on true positives.
– ROC curve: focus on true positives vs. false positives.

These are good tools for evaluation and Stephanie does a good job explaining each.

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Building an Image Classifier with PyTorch

Published 2019-07-19 by Kevin Feasel

Rogier van der Geer shows how you can use PyTorch to build out a Convolutional Neural Network for image classification:

The tool that we are going to use to make a classifier is called a convolutional neural network, or CNN. You can find a great explanation of what these are right here on wikipedia.

But we are not going to fully train one ourselves: that would take way more time than I would be willing to spend. Instead, we are going to do transfer learning, where we take a pre-trained CNN and replace only the last layer by a layer of our own. Then we only need to train that single layer, as all the other layers already have weights that are quite sensible. Here we exploit the fact that the images we are interested in have a lot of the same properties as those images that the original network was trained on. You can find a great explanation of transfer learning here.

Read on for a detailed example.

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