At the end of the last section, we already hypothesized why machine learning managed to diverge in vacabulary from statistcs. Let me begin this section by discussing why the core ideas converge in essence. Many statistical methods crave to prae e videre, that is Latin for to see something that did not happen yet before it actually does, or simply, predict.

Prediction tasks, as other pattern recognition duties, often require a very sharp ability to comprehend data and generalize well into yet unseen information. This sort of shared goal drove the distinct efforts from traditional statistics and machine learning to many common places. Also, statistics, virtue to conceive all sorts of events in a probabilistic way makes it very useful to machine learning, which could be another source of shared ground acrross the different fields, not to mention the interdisciplinary nature of machine learning.

No matter the reason for that, machine learning vocabulary can be adapted and understood through statistics. This translation makes it especially easy for lovers of statistics to master machine learning and vice versa. The paper, Neural Networks and Statistical Models, written by Warren S. Sarle and published in 1994, showed how machine learning jargon could be related to statistical jargon. Here are some jargons:

Now that we acknowledge the existence of a link between statistics and machine learning, the time is coming to take a practical tour through the traditional methods of linear regression given by statistics using our beloved R—but not before examining the general tasks that machine learning is up to.