Supervised Learning

Supervised machine learning techniques involve training a model to operate on a set of features and predict a label using a dataset that includes some already-known label values. You can think of this function like this, in which y represents the label we want to predict, and X represents the vector of features the model uses to predict it.

y = f([X1,X2,X3,...])

Types of Supervised Learning

Supervised learning is classified into two categories of algorithms:

• Regression: Regression algorithms are used to predict a continuous numerical output (e.g., house price, size of dog boot based on harness size). more…

• Classification: Classification algorithms are used to predict a categorical output (e.g., whether an email is spam or not). Classification algorithms predict to which category, or class, an observation belongs. The (y) value in a classification model is a vector of probability values between 0 and 1, one for each class, indicating the probability of the observation belonging to each class. more…