Category: Data Science

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

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.

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.

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.

Valerio Gherardi takes us through the concept of k-grams:

The post is structured as follows: we start by giving a succinct theoretical introduction to kk-gram models. Subsequently, we illustrate how to train a kk-gram model in R using kgrams, and explain how to use the standard perplexity metric for model evaluation or tuning. Finally, we use our trained model to generate some random text at different temperatures.

This goes into some depth on the topic and is worth giving a careful read.

Nathaniel Schmucker explains some of the principles of k-means clustering:

k-Means is easy to implement. In R, you can use the function kmeans() to quickly deploy an efficient k-Means algorithm. On datasets of reasonable size (thousands of rows), the kmeans function runs in fractions of a second.

k-Means is easy to interpret (in 2 dimensions). If you have two features of your k-Means analysis (e.g., you are grouping by length and width), the result of the k-Means algorithm can be plotted on an xy-coordinate system to show the extent of each cluster. It’s easy to visually inspect the assignment to see if the k-Means analysis returned a meaningful insight. In more dimensions (e.g., length, width, and height) you will need to either create a 3D plot, summarize your features in a table, or find another alternative to describing your analysis. This loses the intuitive power that a 2D k-Means analysis has in convincing you or your audience that your analysis should be trusted. It’s not to say that your analysis is wrong; it simply takes more mental focus to understand what your analysis says.

The k-Means analysis, however, is not always the best choice. k-Means does well on data that naturally falls into spherical clusters. If your data has a different shape (linear, spiral, etc.), k-Means will force clustering into circles, which can result in outputs that defy human expectations. The algorithm is not wrong; we have fed the algorithm data it was never intended to understand.

There’s a lot of depth in this article which makes it really interesting.