Machine Learning Overview (page 2 of 13)

It is important to understand the "machine learning lingo" to converse effectively with other data analysts and to be able to translate your processes and results into plain English that users and executives can understand. The machine learning language is different from traditional inferential statistics.

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 training 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 one or more machine learning models. We might say that we trained an ensemble random forest model, which means we used the random forest algorithm to construct our predictive model.