There are four major categories of machine learning algorithms:
Supervised learning: In supervised learning, we have a bunch of data that has already been labeled, and can be used to train a model, which can later be used to predict the labels of new and un-labeled data. A simple example could be data on a list of customers who have previously churned, or people who have defaulted on their loans. We can use this data to train a model, and understand the behaviors demonstrated by churners or loan-defaulters. Once we have trained a model, we can use this model to detect churners or loan-defaulters by looking at similar attributes, and identifying the likelihood of a person being a churner or a loan defaulter. This is also sometimes known as predictive modeling or predictive analytic. Example algorithms include:
- Decision trees
- Regression
- Neural networks
- SVM
Figure 6.3: Supervised learning
Unsupervised learning: In unsupervised learning, there is no pre-existing data with...
