Choosing Right Estimator

Picture yourself back in school, bracing for exams in a couple of weeks. How will you prepare for your exams? Some strategies you may utilize include reading text books or class notes, scavenging Google for resources, or watching videos. Ultimately each person prefers their own method of learning, and if one method does not work, you will try another one until you find one that best suits you.

Most of the time results are how we gage the efficacy of each method- we continue study techniques that yield higher exam scores and abandon techniques that do not translate well to exams. This also means that depending on the exam, different studying methods will need to be used to achieve the most desirable results.

If this is how humans learn, do machines also have different learning preferences in order to achieve the goals they are programmed for? The answer is yes, machines do have different methods of learning depends on the problem which is given to them. Before getting into the methods machines use, it is first important to understand the different types of problems they can be given. For the most part, the two types of problems given to machines can be categorized as unsupervised and supervised.

Unsupervised machine learning models classify data inputs into clusters. For example, an unsupervised problem for machines could be categorizing people with high credit scores and high salaries as one group and people with low credit score and high salary as another. Glancing through an unsupervised problem, some techniques…