Fallacies of Data Science

Adnan Masood and David Lazar have a list of fallacies in the world of data science:

Extrapolating beyond the range of training data, especially in the case of time series data, is fine providing the data-set is large enough.

Strong Evidence is same as a Proof! Prediction intervals and confidence intervals are the same thing, just like statistical significance and practical significance.

These are some good things to think about if you’re getting into analytics.

Related Posts


John Mount explains the vtreat package that he and Nina Zumel have put together: When attempting predictive modeling with real-world data you quicklyrun into difficulties beyond what is typically emphasized in machine learning coursework: Missing, invalid, or out of range values. Categorical variables with large sets of possible levels. Novel categorical levels discovered during test, cross-validation, or […]

Read More

Wrapping Up A Data Science Project

I have finished my series on launching a data science project.  First, I have a post on deploying models as microservices: The other big shift is a shift away from single, large services which try to solve all of the problems.  Instead, we’ve entered the era of the microservice:  a small service dedicated to providing […]

Read More


May 2016
« Apr Jun »