Applying Supervised Learning
When to Consider Supervised Learning
A supervised learning algorithm takes a known set of input data (the training set) and known responses to the data (output), and trains a model to generate reasonable predictions for the response to new input data. Use supervised learning if you have existing data for the output you are trying to predict.
All supervised learning techniques are a form of classification or regression.
Classification techniques predict discrete responses—for example, whether an email is genuine or spam, or whether a tumor is small, medium, or large. Classification models are trained to classify data into categories. Applications include medical imaging, speech recognition, and credit scoring.
Regression techniques predict continuous responses—for example, changes in temperature or fluctuations in electricity demand. Applications include forecasting stock prices, handwriting recognition, and acoustic signal processing.
Selecting the Right Algorithm
As we saw in chapter 1, selecting a machine learning algorithm is a process of trial and error. It’s also a tradeoff between specific characteristics of the algorithms, such as:
- Speed of training
- Memory usage
- Predictive accuracy on new data
- Transparency or interpretability (how easily you can understand the reasons an algorithm makes its predictions)