Machine Learning simplified...(part 4)

Photo by Thomas Kvistholt on Unsplash

(Y = mX + B) does it ring a bell?

The model I just wrote represent what is called a simple linear model or the equation of a straight line. This model is used to fit a relationship among an input variable and an output variable. The model would help in predicting the next value the will fall in the same line (recall your algebra class). Extending such a model (adding more variables) would change its name to a multiple linear model. Both models are easy in interpreting and explaining relationships. Things get a bit complicated though when we shift to nonlinear models (such as polynomial models or Log-linear models). However, these models have better prediction qualities when dealing with complex models and relationships.

 With globalization and business complexities as a result, decision makers are facing not one or few variables that impact their businesses, but 100’s or 1000’s of them. I strongly believe that decision makers have no choice but to embrace machine learning techniques to better manage their business.

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Until next time…