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Machine Learning Automation with TPOT

Machine Learning Automation with TPOT

By : Radečić
4.6 (7)
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Machine Learning Automation with TPOT

Machine Learning Automation with TPOT

4.6 (7)
By: Radečić

Overview of this book

The automation of machine learning tasks allows developers more time to focus on the usability and reactivity of the software powered by machine learning models. TPOT is a Python automated machine learning tool used for optimizing machine learning pipelines using genetic programming. Automating machine learning with TPOT enables individuals and companies to develop production-ready machine learning models cheaper and faster than with traditional methods. With this practical guide to AutoML, developers working with Python on machine learning tasks will be able to put their knowledge to work and become productive quickly. You'll adopt a hands-on approach to learning the implementation of AutoML and associated methodologies. Complete with step-by-step explanations of essential concepts, practical examples, and self-assessment questions, this book will show you how to build automated classification and regression models and compare their performance to custom-built models. As you advance, you'll also develop state-of-the-art models using only a couple of lines of code and see how those models outperform all of your previous models on the same datasets. By the end of this book, you'll have gained the confidence to implement AutoML techniques in your organization on a production level.
Table of Contents (14 chapters)
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1
Section 1: Introducing Machine Learning and the Idea of Automation
3
Section 2: TPOT – Practical Classification and Regression
8
Section 3: Advanced Examples and Neural Networks in TPOT

Exploring the dataset

There is no reason to go wild with the dataset. Just because we can train neural network models with TPOT doesn't mean we should spend 50+ pages exploring and transforming needlessly complex datasets.

For that reason, you'll use a scikit-learn built-in dataset throughout the chapter – the Breast cancer dataset. This dataset doesn't have to be downloaded from the web as it comes built-in with scikit-learn. Let's start by loading and exploring it:

  1. To begin, you'll need to load in a couple of libraries. We're importing NumPy, pandas, Matplotlib, and Seaborn for easy data analysis and visualization. Also, we're importing the load_breast_cancer function from the sklearn.datasets module. That's the function that will load in the dataset. Finally, the rcParams module is imported from Matplotlib to make default styling a bit easier on the eyes:
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt...

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