Models are simplification or approximation of reality and hence they will not capture all of reality. “All models are wrong, but some are useful” is a famous quote by George Edward Pelham Box (1919–2013). George Box was a British mathematician and professor of statistics at the University of Wisconsin. Statisticians develop theoretical models to predict the behaviour of certain process. The meaning of this quote is that every single model will be wrong and it never represents the exact…

ContinueAdded by Janardhanan PS on January 20, 2020 at 11:30pm — 1 Comment

Machine Learning (ML) development is an iterative process in which the accuracy of predictions made by the models is continuously improved by repeating the training and evaluation phases. In each of these iterations, certain parameters are tweaked continuously by developers. Any parameter manually selected based on learning from previous experiments qualify to be called a model hyper-parameter. These parameters represent intuitive decisions whose value cannot be estimated from data or from…

ContinueAdded by Janardhanan PS on January 16, 2020 at 12:00am — 1 Comment

We have data-driven decision support systems implemented in Management Information Systems(MIS). Algorithms created by humans coded into MIS chew raw data and spit out decisions. The MIS systems were developed by human with substantial effort in software development. Once created, they allowed very little flexibility in deriving insights from new data sets. Now we have Machine Learning (ML) systems capable of making data-driven decisions or predictions without the need for explicit…

ContinueAdded by Janardhanan PS on January 8, 2020 at 7:00pm — No Comments

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