Machine Learning Overview (page 2 of 13)

It is important to understand the "machine learning lingo" in order to converse with other data analysts and to be able to translate your processes and results into plain English that users and executives can understand.

Machine learning is the process of using a variety of algorithms (i.e., processes, sets of rules, regressors, or classifiers) to construct a predictive model to make accurate predictions about some phenomenon based on the data that get fed into the algorithm.

Examples of machine learning algorithms include random forests, decision trees, & support vector machines. Using one or more of these algorithms, we train and validate a machine learning model. We might say that we trained a random forest model, which means we used the random forest algorithm to construct our predictive model.