In this chapter
- what is classification
- sentiment analysis: how to tell if a sentence is happy or sad using machine learning
- how to draw a line that separates points of two colors
- what is a perceptron, and how do we train it
- coding the perceptron algorithm in Python and Turi Create
In this chapter, we learn a branch of machine learning called classification. Classification models are similar to regression models, in that their aim is to predict the labels of a dataset based on the features. The difference is that regression models aim to predict a number, whereas classification models aim to predict a state or a category. Classification models are often called classifiers, and we’ll use the terms interchangeably. Many classifiers predict one of two possible states (often yes/no), although it is possible to build classifiers that predict among a higher number of possible states. The following are popular examples of classifiers:
- A recommendation model that predicts whether a user will watch a certain movie
- An email model that predicts whether an email is spam or ham
- A medical model that predicts whether a patient is sick or healthy
- An image-recognition model that predicts whether an image contains an automobile, a bird, a cat, or a dog
- A voice recognition model that predicts whether the user said a particular command