Understanding DiagFit #2 : a cyclic example
In the previous article, we saw how to recognize a problem containing cyclic data. Now we are going to deal with a concrete case of a problem containing cyclic data. We will use our feature generation software designed for cyclic problems, and observe what gains such a feature generator brings when used in combination with […]
Understanding DiagFit #1: Data Cyclicity
When dealing with a data science problem, not all projects are the same. Depending on the data and the objective, different methodologies are applicable to the problem. The best methodology depends on many characteristics of the problem: amount of data, presence of a failure history, etc. Here we will see one of the criteria used […]
Applying the RoCE model to failure prediction
My friend Stephen Allott (we worked together at Micromuse) sent me a few weeks ago the post he wrote about the benefits of the RoCE model (Return on Capital Employed). Originally designed by McKinsey some years ago, He used it to help startups’ customers to calculate the return on the capital they invest in technologies. […]
