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Supervised Machine Learning with Python
Supervised Machine Learning with Python

Supervised Machine Learning with Python: Develop rich Python coding practices while exploring supervised machine learning

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Supervised Machine Learning with Python

Implementing Parametric Models

In the previous chapter, we got started with the basics of supervised machine learning. In this chapter, we will dive into the guts of several popular supervised learning algorithms within the parametric modeling family. We'll start this chapter by formally introducing parametric models. Then, we'll introduce two very popular parametric models: linear and logistic regression. We'll spend some time looking at their inner workings and then we'll jump into Python and actually code those workings from scratch.

In this chapter, we will cover the following topics:

  • Parametric models
  • Implementing linear regression from scratch
  • Logistic regression models
  • Implementing logistic regression from scratch
  • The pros and cons of parametric models

Technical requirements

Parametric models

When it comes to supervised learning, there are two families of learning algorithms: parametric and non-parametric. This area also happens to be a hotbed for gatekeeping and opinion-based conjecture regarding which is better. Basically, parametric models are finite-dimensional, which means that they can learn only a defined number of model parameters. Their learning stage is typically categorized by learning some vector theta, which is also called a coefficient. Finally, the learning function is often a known form, which we will clarify later in this section.

Finite-dimensional models

If we go back to our definition of supervised learning, recall that we need to learn some function, f. A parametric...

Implementing linear regression from scratch

Linear regression solves the least squares equation to discover the parameters vector theta. In this section, we will walk through the source code for a linear regression class in the packtml Python library and then cover a brief graphical example in the examples directory.

Before we look at the code, we will be introduced to the interface that backs all of the estimators in the book. It is called BaseSimpleEstimator, which is an abstract class. It's going to enforce only one method, which is predict. Different subclass layers are going to enforce other methods for different model families. But this layer backs all the models that we will build, as everything that we are putting together is supervised, so it's all going to need to be able to predict. You will notice that the signature is prescribed in the dock string. Every...

Logistic regression models

In this section, we will look at logistic regression, which is the first hill-climbing algorithm that we'll cover, and we will have a brief recap of linear regression. We will also look at how logistic regression differs both mathematically and conceptually. Finally, we will learn the core algorithm and explain how it makes predictions.

The concept

Logistic regression is conceptually the inverse of linear regression. What if, rather than a real value, we want a discrete value or a class? We have already seen one example of this type of question early on when we wanted to predict whether or not an email was spam. So, with logistic regression, rather than predicting a real value, we can predict...

Implementing logistic regression from scratch

In this section, we will walk through the implementation of logistic regression in Python within the packtml package. We will start off with a brief recap of what logistic regression seeks to accomplish and then go over the source code and look at an example.

Recall that logistic regression seeks to classify a sample into a discrete category, also known as classification. The logistic transformation allows us to transform the log odds that we get from the inner product of our parameters and X.

Notice that we have three Python files open. One is extmath.py, from within the utils directory inside of packtml; another is simple_logistic.py, from within the regression library in packtml; and the final one is an example_logistic_regression.py file, inside the examples directory and regression.

We will dive right into the code base...

The pros and cons of parametric models

Parametric models have some really convenient attributes. Namely, they are fast to fit, don't require too much data, and can be very easily explained. In the case of linear and logistic regression, it's easy to look at coefficients and directly explain the impact of fluctuating one variable in either direction. In regulated industries, such as finance or insurance, parametric models tend to reign supreme, since they can be easily explained to regulators. Business partners tend to really rely on the insights that the coefficients produce. However, as is evident in what we've already seen so far, they tend to oversimplify. So, as an example, the logistic regression decision boundary that we looked at in the last section assumes a perfect linear boundary between two classes.

It is rare that the real world can be constrained into...

Technical requirements


For this chapter, you will need to install the following software, if you haven't already done so:

  • Jupyter Notebook
  • Anaconda
  • Python

 

 

The code files for this chapter can be found at https://github.com/PacktPublishing/Supervised-Machine-Learning-with-Python.

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Key benefits

  • Delve into supervised learning and grasp how a machine learns from data
  • Implement popular machine learning algorithms from scratch
  • Explore some of the most popular scientific and mathematical libraries in the Python language

Description

Supervised machine learning is used in a wide range of sectors, such as finance, online advertising, and analytics, to train systems to make pricing predictions, campaign adjustments, customer recommendations, and much more by learning from the data that is used to train it and making decisions on its own. This makes it crucial to know how a machine 'learns' under the hood. This book will guide you through the implementation and nuances of many popular supervised machine learning algorithms, and help you understand how they work. You’ll embark on this journey with a quick overview of supervised learning and see how it differs from unsupervised learning. You’ll then explore parametric models, such as linear and logistic regression, non-parametric methods, such as decision trees, and a variety of clustering techniques that facilitate decision-making and predictions. As you advance, you'll work hands-on with recommender systems, which are widely used by online companies to increase user interaction and enrich shopping potential. Finally, you’ll wrap up with a brief foray into neural networks and transfer learning. By the end of this book, you’ll be equipped with hands-on techniques and will have gained the practical know-how you need to quickly and effectively apply algorithms to solve new problems.

Who is this book for?

This book is for anyone who wants to get started with supervised learning. Intermediate knowledge of Python programming along with fundamental knowledge of supervised learning is expected.

What you will learn

  • Crack how a machine learns a concept and generalizes its understanding of new data
  • Uncover the fundamental differences between parametric and non-parametric models
  • Implement and grok several well-known supervised learning algorithms from scratch
  • Work with models in domains such as ecommerce and marketing
  • Get to grips with algorithms such as regression, decision trees, and clustering
  • Build your own models capable of making predictions
  • Delve into the most popular approaches in deep learning such as transfer learning and neural networks
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Table of Contents

5 Chapters
First Step Towards Supervised Learning Chevron down icon Chevron up icon
Implementing Parametric Models Chevron down icon Chevron up icon
Working with Non-Parametric Models Chevron down icon Chevron up icon
Advanced Topics in Supervised Machine Learning Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(2 Ratings)
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charles drotar Aug 08, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This book was a great reference for me and is very well written! Given my experience productizing Python ML models, I found this would have been a great educational tools for Data Scientists I had supported in the past. It would have helped to establish better coding practices. Definitely worth the money!
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Aaron Smith Aug 02, 2019
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I am not a data scientist/machine learning engineer by any means, but I am interested in the field. This book was super easy to follow along with, and provides great real-world examples. Having previously gone through the Packt video course of the same name, this was a great reference to add to my shelf.
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