Chapter 4 Modeling and prediction

Chapter 3 from Real-world Machine Learning by Henrik Brink, Joseph W. Richards, and Mark Fetherolf.

Discovering relationships in data through ML modeling

Using models for prediction and inference

Building classification models

Building regression models

The previous chapter covered guidelines and principles of data collection, preprocessing, and visualization. The next step in the machine-learning workflow is to use that data to begin exploring and uncovering the relationships that exist between the input features and the target. In machine learning, this process is done by building statistical models based on the data. This chapter covers the basics required to understand ML modeling and to start building your own models. In contrast to most machine-learning textbooks, we spend little time discussing the various approaches to ML modeling, instead focusing attention on the big-picture concepts. This will help you gain a broad understanding of machine-learning model building and quickly get up to speed on building your own models to solve real-world problems.

For those seeking more information about specific ML modeling techniques, please see the appendix.

3.1  Basic machine-learning modeling

3.1.2  The purpose of finding a good model

3.1.3  Types of modeling methods

3.2  Classification: predicting into buckets

3.2.1  Building a classifier and making predictions

3.2.2  Classifying complex, nonlinear data

3.2.3  Classifying with multiple classes

3.3  Regression: predicting numerical values

3.3.1  Building a regressor and making predictions

3.3.2  Performing regression on complex, nonlinear data

3.4  Summary

3.5  Terms from this chapter