Selecting good features – Part IV: stability selection, RFE and everything side by side

In my previous posts, I looked at univariate methods,linear models and regularization and random forests for feature selection.In this post, I’ll look at two other methods: stability selection and recursive feature elimination (RFE), which can both considered wrapper methods.

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Amazon Redshift Performance – Bigger Clusters, or Bigger Nodes?

Last week, I looked into options for increasing the performance of an Amazon Redshift cluster that was currently using 10 large dense compute nodes (dc1.large). While investigating, I noticed that a cluster of 32 dc1.large nodes (the maximum for that node type) had the same number of CPUs, the same amount of storage, and a comparable amount of RAM to a cluster of 2 dc1.

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Do AIs dream of pwning FF leagues?

In a previous post, I looked at how the established value based drafting (VBD) algorithm for picking fantasy football rosters would perform in a league of typical human players.  It turned out that we get different performance depending on if we look at ranks of VBD drafters based (i) on expected preseason player forecasts or (ii) on actual points scored by a player that season.

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Keeping Your Autonomous Data Warehouse Secure with Data Safe – Part 2

In the part 1 of this series we looked at how to get your ADW and OCI environment ready for using Data Safe. If you missed part 1 or need a quick refresher then the blog post is here: https://blogs.oracle.com/datawarehousing/keeping-your-autonomous-data-warehouse-secure-with-data-safe-part-1.

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Just evaluating a polynomial: how hard could it be?

The previous post looked at an example of strange floating point behavior taking from book End of Error. This post looks at another example.
This example, by Siegfried Rump, asks us to evaluate
333.75 y6 + x2 (11 x2y2 – y6 – 121 y4 – 2) + 5.5 y8 + x/(2y)
at x = 77617 and y = 33096.
Here we evaluate Rump’s example in single, double, and quadruple precision.

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