mrandrewandrade

I think we are saying the same thing in different words, and I might be confusing "an alternative" with "a comparison". John compares R^2 with E^2, but RMSE can be considered an alternative to using R^2 in certain cases.

If you go back to the first line: > People sometimes use R^2 as their preferred measure of model fit.

I think the post is going over why R^2 is not recommended as 2 is not only a measure of the error, but it includes a comparison with a constant model. John defines E^2 as a comparison metric which measures how much worse the errors are than if you used the true model.

Going back to a metric for determining model fit, RMSE/MSE/MAD are all alternative measures of model fit and are useful depending on the dataset.

I think what John is saying is that people commonly use R^2 as measure of model fit where something like root mean squared error (RMSE) gives a better measure of model fit (by measuring the distance from the true model) depending on the model. Just using R^2 blindly for most tasks you would work on can lead to choosing an incorrect model.

I think the main take-away from the post is to better understand the correct measure for model fit for your specific data. For example, if you are forecasting a time series with stationary demand, mean average deviation might be the best measure of model fit, but it the case where there is seasonality with a trend, the RMSE would be a better measure [1].

[1] http://robjhyndman.com/papers/foresight.pdf

The final demo didn't include any code. Writing the code for the arduino was a waste of time in terms of what we actually build. The main part of the post was about the pivot and how we won with a simplified codeless solution

Great by post by Samson! I sometime wish I could tell stories and explain like he can

Another great graphical way to play with data in Python is using Spyder. It has an Rstudio/matlab sort of interface

https://code.google.com/p/spyderlib/

http://en.wikipedia.org/wiki/Spyder_(software)

Haha thanks. The point of the blog was that we made the design too complicated wanting a microcontroller and innate intelligence in the tool when really all we need were flashing LED's