Category: Data Science

Calculating Relative Risk In T-SQL

Published 2017-06-20 by Kevin Feasel

Mala Mahadevan explains how to calculate relative risk using T-SQL:

In this post we will explore a common statistical term – Relative Risk, otherwise called Risk Factor. Relative Risk is a term that is important to understand when you are doing comparative studies of two groups that are different in some specific way. The most common usage of this is in drug testing – with one group that has been exposed to medication and one group that has not. Or , in comparison of two different medications with two groups with each exposed to a different one.

Read on for an example of a statistical formula calculation which might actually be easier in T-SQL than R.

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Random Forests In scikit-learn

Published 2017-06-20 by Kevin Feasel

Mark Needham shows how easy it is to create a random forest model in Python using scikit-learn:

As I mentioned in a blog post a couple of weeks ago, I’ve been playing around with the Kaggle House Prices competition and the most recent thing I tried was training a random forest regressor.
Unfortunately, although it gave me better results locally it got a worse score on the unseen data, which I figured meant I’d overfitted the model.
I wasn’t really sure how to work out if that theory was true or not, but by chance, I was reading Chris Albon’s blog and found a post where he explains how to inspect the importance of every feature in a random forest. Just what I needed!

There’s a nagging voice in my head saying “Principal Component Analysis” as I read this post.

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Fraud Detection With Python

Published 2017-06-19 by Kevin Feasel

Kevin Jacobs has a walkthrough of how to use Pandas and scikit-learn to perform fraud detection against a sample set of credit card transactions:

Apparently, the data consists of 28 variables (V1, …, V28), an “Amount” field a “Class” field and the “Time” field. We do not know the exact meanings of the variables (due to privacy concerns). The Class field takes values 0 (when the transaction is not fraudulent) and value 1 (when a transaction is fraudulent). The data is unbalanced: the number of non-fraudulent transactions (where Class equals 0) is way more than the number of fraudulent transactions (where Class equals 1). Furthermore, there is a Time field. Further inspection shows that these are integers, starting from 0.
There is a small trick for getting more information than only the raw records. We can use the following code:
print(df.describe())
This code will give a statistically summary of all the columns. It shows for example that the Amount field ranges between 0.00 and 25691.16. Thus, there are no negative transactions in the data.

The Kaggle competition data set is available, so you can follow along.

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Forcing 0 Intercept Inflates R-squared In R

Published 2017-06-19 by Kevin Feasel

John Mount has an informative post on how you can trick yourself when running linear regression models in R and forcing the y intercept to be 0:

So far so good. Let’s now remove the “intercept term” by adding the “0+” from the fitting command.
m2 <- lm(y~0+x, data=d)t(broom::glance(m2))
## [,1]
## r.squared 7.524811e-01
## adj.r.squared 7.474297e-01
## sigma 3.028515e-01
## statistic 1.489647e+02
## p.value 1.935559e-30
## df 2.000000e+00
## logLik -2.143244e+01
## AIC 4.886488e+01
## BIC 5.668039e+01
## deviance 8.988464e+00
## df.residual 9.800000e+01
d$pred2 <- predict(m2, newdata = d)
Uh oh. That appeared to vastly improve the reported R-squared and the significance (“p.value“)!

Read on to learn why this happens and how you can prevent this from tricking you in the future.

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Facial Recognition With Amazon Rekognition

Published 2017-06-16 by Kevin Feasel

Chris Adzima describes how his law enforcement agency uses Amazon Rekognition for facial recognition:

Setup was fairly straightforward. In the Washington County jail management system (JMS), we have an archive of mugshots going back to 2001. We needed to get the mugshots (all 300,000 of them) into Amazon S3. Then we need to index them all in Amazon Rekognition, which took about 3 days.
Our JMS allows us to tag the shots with the following information: front view or side view, scars, marks, or tattoos. We only wanted the front view, so we used those tags to get a list of just those.

Read on for sample implementation details, including moving images to S3, building the facial recognition “database,” and using it.

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Cochran-Mantel-Haenszel Test

Published 2017-06-12 by Kevin Feasel

Mala Mahadevan explains the Cochran-Mantel-Haenszel test, with two parts up so far. First, her data set:

Below is the script to create the table and dataset I used. This is just test data and not copied from anywhere.

Second, an introduction to the test itself and solutions in R and T-SQL:

This test is an extension of the Chi Square test I blogged of earlier. This is applied when we have to compare two groups over several levels and comparison may involve a third variable.
Let us consider a cohort study as an example – we have two medications A and B to treat asthma. We test them on a randomly selected batch of 200 people. Half of them receive drug A and half of them receive drug B. Some of them in either half develop asthma and some have it under control. The data set I have used can be found here. The summarized results are as below.

This series is not yet complete, so stay tuned.

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Quantile Regression With Python

Published 2017-06-09 by Kevin Feasel

Gopi Subramanian discusses one of my favorite regression concepts, heteroskedasticity:

With variance score of 0.43 linear regression did not do a good job overall. When the x values are close to 0, linear regression is giving a good estimate of y, but we near end of x values the predicted y is far way from the actual values and hence becomes completely meaningless.
Here is where Quantile Regression comes to rescue. I have used the python package statsmodels 0.8.0 for Quantile Regression.
Let us begin with finding the regression coefficients for the conditioned median, 0.5 quantile.

The article doesn’t render the code very well at all, but Gopi does have the example code on Github, so you can follow along that way.

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Sentiment Analysis In R

Published 2017-06-07 by Kevin Feasel

Stefan Feuerriegel and Nicolas Pröllochs have a new package in CRAN:

Our package “SentimentAnalysis” performs a sentiment analysis of textual contents in R. This implementation utilizes various existing dictionaries, such as QDAP or Loughran-McDonald. Furthermore, it can also create customized dictionaries. The latter uses LASSO regularization as a statistical approach to select relevant terms based on an exogenous response variable.

I’m not sure how it stacks up to external services, but it’s another option available to us.

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Understanding Random Forests

Published 2017-06-02 by Kevin Feasel

Manish Kumar Barnwal explains how random forest algorithms work:

Say our dataset has 1,000 rows and 30 columns. There are two levels of randomness in this algorithm:
At row level: Each of these decision trees gets a random sample of the training data (say 10%) i.e. each of these trees will be trained independently on 100 randomly chosen rows out of 1,000 rows of data. Keep in mind that each of these decision trees is getting trained on 100 randomly chosen rows from the dataset i.e they are different from each other in terms of predictions.
At column level: The second level of randomness is introduced at the column level. Not all the columns are passed into training each of the decision trees. Say we want only 10% of columns to be sent to each tree. This means a randomly selected 3 column will be sent to each tree. So for the first decision tree, may be column C1, C2 and C4 were chosen. The next DT will have C4, C5, C10 as chosen columns and so on.

This is a nice article and includes cases when not to use random forests.

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Multi-Shot Games

Published 2017-05-26 by Kevin Feasel

Dan Goldstein explains a counter-intuitive probability exercise:

Peter Ayton is giving a talk today at the London Judgement and Decision Making Seminar
Imagine being obliged to play Russian roulette – twice (if you are lucky enough to survive the first game). Each time you must spin the chambers of a six-chambered revolver before pulling the trigger. However you do have one choice: You can choose to either (a) use a revolver which contains only 2 bullets or (b) blindly pick one of two other revolvers: one revolver contains 3 bullets; the other just 1 bullet. Whichever particular gun you pick you must use every time you play. Surprisingly, option (b) offers a better chance of survival

My recommendation is to avoid playing Russian roulette.

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