After review, I have come to the conclusion that from a predictive and operational standpoint, causal explanations translate to directed graphs, such as the following: And I think it is interesting the machine learning community focuses on causal explanations for “manipulation” to guide reactive and interactive machines, and that directed graphs (or perhaps a Bayesian … Continue reading Granger Causality
Big Data: New Tricks for Econometrics is, for my money, one of the best discussions of techniques like classification and regression trees, random forests, and penalized regression (such as lasso, lars, and elastic nets) that can be found. Varian, pictured aove, is emeritus professor in the School of Information, the Haas School of Business, and … Continue reading Hal Varian and the “New” Predictive Techniques
You might imagine that there is an iron law of ordinary least squares (OLS) regression – the number of observations on the dependent (target) variable and associated explanatory variables must be less than the number of explanatory variables (regressors). Ridge regression is one way to circumvent this requirement, and to estimate, say, the value of … Continue reading The Problem of Many Predictors – Ridge Regression and Kernel Ridge Regression
More than 25,000 visited businessforecastblog, March 2012-December 2013, some spending hours on the site. Interest ran nearly 200 visitors a day in December, before my ability to post was blocked by a software glitch, and we did this re-boot. Now I have hundreds of posts offline, pertaining to several themes, discussed below. How to put this material … Continue reading The On-Coming Tsunami of Data Analytics
