before we delve into unsupervised learning systemslet' take look at supervised learning algorithms and how they work this will help frame where unsupervised learning fits within the machine learning ecosystem in supervised learningthere are two major types of problemsclassification and regression in classificationthe ai must correctly classify items into one of two or more classes if there are just two classesthe problem is called binary classification if there are three or more classesthe problem is classed multiclass classification classification problems are also known as discrete prediction problems because each class is discrete group classification problems also may be referred to as qualitative or categorical problems in regressionthe ai must predict continuous variable rather than discrete one regression problems also may be referred to as quantitative problems supervised machine learning algorithms span the gamutfrom very simple to very complexbut they are all aimed at minimizing some cost function or error rate (or maximizing value functionthat is associated with the labels we have for the dataset as mentioned beforewhat we care about most is how well the machine learning solution generalizes to never-before-seen cases the choice of the supervised learning algorithm is very important at minimizing this generalization error to achieve the lowest possible generalization errorthe complexity of the algorithmic model should match the complexity of the true function underlying the data we do not know what this true function really is if we didwe would not need to use machine learning to create model--we would just solve the function to find the right answer but since we do not know what this true function iswe choose machine learning algorithm to test hypotheses and find the model that best approximates this true function ( has the lowest possible generalization errorif what the algorithm models is less complex than the true functionwe have underfit the data in this casewe could improve the generalization error by choosing an algorithm that can model more complex function howeverif the algorithm designs an overly complex modelwe have overfit the training data and will have poor performance on never-before-seen casesincreasing our generalization error in other wordschoosing more complex algorithms over simpler ones is not always the right choice--sometimes simpler is better each algorithm comes with its set of strengthsweaknessesand assumptionsand knowing what to use when given the data you have and the problem you are trying to solve is very important to mastering machine learning unsupervised learning in the machine learning ecosystem