Category: Data Science

Edwin Thoen gives us a few tips for dealing with failing data science projects:

At the beginning of a project the levels enthusiasm and optimism are always at its peak. Especially in data science projects. Isn’t data the new oil? This is the time we are finally going to dig into that well and leverage our data in unprecedented ways! No setbacks are experienced yet. There is only one road ahead and it will lead us to success. Probably at this stage you, the data scientist, are already well aware of a number of project risks. You might want to keep these concerns to yourself, as you don’t want to come across as negative, or worse, someone who is not up to the job ahead. Please don’t, if you foresee possible problems at this stage and you don’t speak out, they can come back as a boomerang when the problems actually occur. Rather, invite all stakeholders to perform a risk analysis together.

This is good advice and applies outside of data science projects as well.  H/T R-bloggers

John Mount explains how to perform bias correction and explains why it happens so rarely in practice:

The bias in question is falling off at a rate of 1/n (where n is our sample size). So the bias issue loses what little gravity it ever may have ever had when working with big data. Most sources of noise will be falling off at a slower rate of 1/sqrt(n), so it is unlikely this bias is going to be the worst feature of your sample.

But let’s pretend the sample size correction indeed is an important point for a while.

Under the “no bias allowed” rubric: if it is so vitally important to bias-correct the variance estimate, would it not be equally critical to correct the standard deviation estimate?

The practical answer seems to be: no. The straightforward standard deviation estimate itself is biased (it has to be, as a consequence of Jensen’s inequality). And pretty much nobody cares, corrects it, or teaches how to correct it, as it just isn’t worth the trouble.

This is a good explanation of the topic as well as the reason people make these corrections so rarely.

Shrin Glander has a new video, currently only in German but there is an English transcript:

RF is based on decision trees. In machine learning decision trees are a technique for creating predictive models. They are called decision trees because the prediction follows several branches of “if… then…” decision splits – similar to the branches of a tree. If we imagine that we start with a sample, which we want to predict a class for, we would start at the bottom of a tree and travel up the trunk until we come to the first split-off branch. This split can be thought of as a feature in machine learning, let’s say it would be “age”; we would now make a decision about which branch to follow: “if our sample has an age bigger than 30, continue along the left branch, else continue along the right branch”. This we would do until we come to the next branch and repeat the same decision process until there are no more branches before us. This endpoint is called a leaf and in decision trees would represent the final result: a predicted class or value.

At each branch, the feature thresholds that best split the (remaining) samples locally is found. The most common metrics for defining the “best split” are gini impurity and information gain for classification tasks and variance reduction for regression.

Click through for more info and if you understand German, the video is good as well.