Book Image

Applied Supervised Learning with Python

By : Benjamin Johnston, Ishita Mathur
Book Image

Applied Supervised Learning with Python

By: Benjamin Johnston, Ishita Mathur

Overview of this book

Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!
Table of Contents (9 chapters)


In the previous three chapters, we discussed the two types of supervised learning problems, regression and classification, followed by ensemble models, which were built from a combination of base models. We built several models and discussed how and why they work.

However, that is not enough to take a model to production. Model development is an iterative process, and the model training step is followed by validation and updating steps:

Figure 6.1: Machine learning model development process

This chapter will explain the peripheral steps in the process shown in the preceding flowchart; we will discuss how to select the appropriate hyperparameters and how to perform model validation using the appropriate error metrics. Improving a model's performance happens by iteratively performing these two tasks.

But why is it important to evaluate your model? Say you've trained your model and provided some hyperparameters, made predictions, and found its accuracy. That's the gist of it, but how...