All you need to know about Classification and Regression (Machine Learning in 270 pages)

In classification problems, we are trying to predict a discrete number of values. The labels(y) generally comes in the categorical form and represents a finite number of classes.

+Decision Trees
+Logistic Regression
+Naive Bayes
+K Nearest Neighbors
+Linear SVC (Support vector Classifier)s.

In regression problems, we try to predict continuous valued output, take this example. Given the size of the house predict the price(real value).

+Regression Algorithms
+Linear Regression
+Regression Trees(e.g. Random Forest)
+Support Vector Regression (SVR)

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