kRiging (Kriging in R) As a programmer, it is often frustrating to solve equations by hand, knowing that a clever implementation in software could solve an infinite variety of similar problems. This cannot be said for the paper-and-pen approach. As a <<Mastère spécialisé>> student in the Cycle de Formation Spécialisée en Géostatistique at Mines Paristech, I have spent the better part of these past few weeks building and solving kriging systems.

Hugo Tips: Part 2 I only have 3 tips this time, but they are important: Always always always remember to run env HUGO_ENV="production" hugo (or the equivalent on your system) rather than just plain hugo. It is possible that running hugo will write the files in development mode, which puts NOCRAWL, NOINDEX tags on every page. These tags will prevent search engines from crawling your site. For Windows, it should be set HUGO_ENV=production.

This is a continuation of my first post on the SEG Facies Classification competition. In the previous post I left off before implementing a baseline learner to see what results we might expect. Baseline Learner After Witten, Frank, and Hall’s Data Mining, I use depth-1 decision stumps and shallow decision trees as baseline learners. Decision trees are easily interpretable once plotted. They also give us a sense of feature importance, since the greedy algorithm will split the more informative features earlier in the tree.