Supervised learning

Supervised learning enables you to train a machine learning model based on training examples that you provide. You can then use your model to make predictions on new data. This page summarizes the end-to-end workflow for training, evaluating and deploying a model. It gives a high-level overview of the steps required to identify and implement a solution using supervised learning.

📄️ Introduction

The workflow for supervised learning consists of the following stages:

📄️ Predicting classes with classification

Classification is a machine learning process that predicts the class or category of a data point in a data set. For a simple example, consider how the shapes in the following graph can be differentiated and classified as "circles" and "triangles":

📄️ Predicting numerical values with regression

Regression analysis is a supervised machine learning process for estimating the relationships among different fields in your data, then making further predictions on numerical data based on these relationships. For example, you can predict the response time of a web request or the approximate amount of data that the server exchanges with a client based on historical data.