Category: R

Holger von Jouanne-Diedrich explains the results of a poll:

Some time ago I conducted a poll on LinkedIn that quickly went viral. I asked which of three different coin tossing sequences were more likely and I received exactly 1,592 votes! Nearly 48,000 people viewed it and more than 80 comments are under the post (you need a LinkedIn account to fully see it here: LinkedIn Coin Tossing Poll).

In this post I will give the solution with some background explanation, so read on!

Read on to understand why it’s just as likely that you’ll see a sequence, when flipping a coin, of H,H,H,H,H,H just as often as you’ll see H,T,H,T,H,T.

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Antoine Rebecq shares a warning:

I was recently working on a cool dataset that looked unusually friendly. It was tidy, neat, interesting… the kind of things that you rarely encounter in the wild! My goal was to build a super simple predictor for one of the features. However, I kept getting poor results and at first couldn’t figure out what was happening.

There’s some good, practical advice in there, so check it out. H/T R-Bloggers

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Bryan Shalloway continues a series:

Part 1 of my series of posts on building prediction intervals used data held-out from model training to evaluate the characteristics of prediction intervals. In this post I will use hold-out data to estimate the width of the prediction intervals directly. Doing such can provide more reasonable and flexible intervals compared to analytic approaches.

Click through for the article, and be sure to check out part 1 if you haven’t already.

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Bryan Shalloway explains how generating prediction intervals is different from making point predictions:

Before using the model for predictive inference, one should have reviewed overall performance on a holdout dataset to ensure the model is sufficiently accurate for the business context. For example, for our problem is an average error of ~12% and 90% prediction intervals of +/- ~25% of Sale_Price useful? If the answer is “no,” that suggests the need for more effort in improving the accuracy of the model (e.g. trying other transformations, features, model types). For our examples we are assuming the answer is ‘yes,’ our model is accurate enough (so it is appropriate to move-on and focus on prediction intervals).

Click through for the article.

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Holger von Jouanne-Diedrich brings the noise:

In data science, we try to find, sometimes well-hidden, patterns (= signal) in often seemingly random data (= noise). Pseudo-Random Number Generators (PRNG) try to do the opposite: hiding a deterministic data generating process (= signal) by making it look like randomness (= noise). If you want to understand some basics behind the scenes of this fascinating topic, read on!

Click through for an explanation of the process.

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