common principle for predictive modeling

nagaswathi's version from 2017-10-14 15:12


Hi, there is a common principle that underlies all supervised machine learning algorithms for predictive modeling.
Machine learning algorithms are described as learning a target function (f) that best maps input variables (X) to an output variable (Y).


Y = f(X)


This is a general learning task where we would like to make predictions in the future (Y) given new examples of input variables (X). We don't know what the function (f) looks like or its form. If we did, we would use it directly and we would not need to learn it from data using machine learning algorithms.


The most common type of machine learning is to learn the mapping Y = f(X) to make predictions of Y for new X. This is called predictive modeling or predictive analytics and our goal is to make the most accurate predictions possible.


In the next lesson, you will discover the difference between parametric and nonparametric algorithms.