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Category: Analytics

Decision Trees vs. Coding on IBM i

Posted on June 20, 2017 by David Gillman

In machine learning, decision trees are a great algorithm family to work with business information. They are not the most precise nor are they considered cutting edge, but they are a first pass algorithm for many data scientists. Maybe in version two of a project, another algorithm family might create a better model for delivering a reliable model, but over most types of transaction or ERP data, decision trees as a class are where most data scientists start. Read more arrow


Missing Data – The “Green” Revolution Continues

Posted on June 13, 2017 by David Gillman

Many machine learning and predictive processes struggle when they encounter missing data; entire records are bypassed if one field value is missing in the algorithm. For example, in a decision tree, if no value exists for the field where the tree splits, that record is useless because the algorithm cannot say what tree branch the record needs to follow. Read more arrow


The “Green” Revolution Rolls On

Posted on June 6, 2017 by David Gillman

Someone messaged me to point out my title of the “Green” Revolution last week might also refer to IBM i and its heritage with green screen terminal interfaces. Read more arrow


Clean Data — A Current “Green” Revolution

Posted on May 30, 2017 by David Gillman

Good quality data is never a bad thing. For fueling analytic processes, it is a must. In order to maximize return on the investment in machine learning and predictive analytics, companies need clean data as a foundation for analysis. (My use of “green” in the title refers to making money for those outside the US.) Read more arrow

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