Be Wary Of Colliders When Analyzing Data

Keith Goldfeld has an interesting demonstration of a collider variable and how it can lead us to incorrect conclusions during analysis:

In this (admittedly thoroughly made-up though not entirely implausible) network diagram, the test score outcome is a collider, influenced by a test preparation class and socio-economic status (SES). In particular, both the test prep course and high SES are related to the probability of having a high test score. One might expect an arrow of some sort to connect SES and the test prep class; in this case, participation in test prep is randomized so there is no causal link (and I am assuming that everyone randomized to the class actually takes it, a compliance issue I addressed in a series of posts starting with this one.)

The researcher who carried out the randomization had a hypothesis that test prep actually is detrimental to college success down the road, because it de-emphasizes deep thinking in favor of wrote memorization. In reality, it turns out that the course and subsequent college success are not related, indicated by an absence of a connection between the course and the long term outcome.

Read the whole thing.  H/T R-Bloggers

XGBoost In R

Fisseha Berhane explains how to implement Extreme Gradient Boosting in R:

What makes it so popular are its speed and performance. It gives among the best performances in many machine learning applications. It is optimized gradient-boosting machine learning library. The core algorithm is parallelizable and hence it can use all the processing power of your machine and the machines in your cluster. In R, according to the package documentation, since the package can automatically do parallel computation on a single machine, it could be more than 10 times faster than existing gradient boosting packages.

xgboost shines when we have lots of training data where the features are numeric or a mixture of numeric and categorical fields. It is also important to note that xgboost is not the best algorithm out there when all the features are categorical or when the number of rows is less than the number of fields (columns).

xgboost is a nice complement to neural networks, as they tend to be great at different things.

Data Cleansing With R

I continue my series on launching a data science project:

Now that we’ve performed some basic analysis, we will clean up the data set. I’m doing most of the cleanup in a single operation, but I do have some comment notes here, particularly around the oddities with SalaryUSD. The SalaryUSD column has a few problems:

  • Some people put in pennies, which aren’t really that important at the level we’re discussing. I want to strip them out.
  • Some people put in delimiters like commas or decimal points (which act as commas in countries like Germany). I want to strip them out, particularly because the decimal point might interfere with my analysis, turning 100.000 to $100 instead of $100K.
  • Some people included the dollar sign, so remove that, as well as any spaces.

It’s not a perfect regex, but it did seem to fix the problems in this data set at least.

Something I’ve liked about the data professionals survey is that there are a few places with room for data cleansing, but not everything is awful.  It’s neither artificially clean nor beyond repair, so it’s good for use as an example.

Dropping Columns With Logstash

Mike Hillwig shows how to ignore columns with Logstash:

Like I said earlier, we have some data that I know I’ll never use. This is flight performance data. The dataset contains diversion information. If a flight gets diverted more than once, it’s tracked here. I don’t care about that, so I’m dropping the diversion information for the second through fifth diversions. I’m also dropping some information about the airports that I believe I won’t need. This is the tricky part. Somewhere down the road, I’m going to need to enhance this data by converting all of the times to UTC.

Mike’s slowly building up to a complete, working example and it’s interesting to watch the progress along the way.

Columnstore And Merge Replication

Niko Neugebauer tests whether merge replicated tables can use columnstore indexes:

Adding this table to the publication will end up with the following, self-explaining error message, being very clear that the Clustered Columnstore Indexes are not supported for the Merge Replication[.]

There is no surprise here, as the same Clustered Columnstore Indexes are not supported for the Transactional Replication, but I feel that a great opportunity is lost and the Replication technology are being quite ignored by the emerged technologies, such as In-Memory & Columnstore, where the scenarios of replicating the Data Warehousing data is something that a lot of people can find very useful.

I wish it would be otherwise, and this would allow to bring more customers to use Columnstore Indexes.

Clustered columnstore indexes aren’t possible, but read on to learn whether non-clustered columnstore indexes are supported.

Trace Flag 834 And Columnstore Tables

Joe Obbish shows how trace flag 834 can solve a bottleneck when inserting into tables with clustered columnstore indexes:

In my experience, when we get into a situation with high memory waits caused by too much concurrent CCI activity all queries on the server that use a memory grant can be affected. For example, I’ve seen sp_whoisactive run for longer than 90 seconds.

It needs to be stated that not all CCIs will suffer from this scalability problem. I was able to achieve good scalability with some artificial tables, but all of the real target tables that I tested have excessive memory waits at high concurrency. Perhaps tables which require more CPU to compress naturally spread out their memory requests and the underlying OS is better able to keep up.

Read the whole thing, and also check out Lonny Niederstadt’s comment as it adds pertinent information about TF834.

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