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The Supervised Learning Workshop

You're reading from The Supervised Learning Workshop Predict outcomes from data by building your own powerful predictive models with machine learning in Python

Product type Paperback

Published in Feb 2020

Publisher Packt

ISBN-13 9781800209046

Length 532 pages

Edition 2nd Edition

Languages

Python

Tools

Jupyter

Concepts

Machine Learning

Authors (4):

Blaine Bateman

Ashish Ranjan Jha

Ishita Mathur

Benjamin Johnston

View More author details

Table of Contents (9) Chapters

Preface

1. Fundamentals

2. Exploratory Data Analysis and Visualization FREE CHAPTER

3. Linear Regression

4. Autoregression

5. Classification Techniques

6. Ensemble Modeling

7. Model Evaluation

Appendix

7. Model Evaluation

Overview

This chapter is an introduction to how you can improve a model's performance by using hyperparameters and model evaluation metrics. You will see how to evaluate regression and classification models using a number of metrics and learn how to choose a suitable metric for evaluating and tuning a model.

By the end of this chapter, you will be able to implement various sampling techniques and perform hyperparameter tuning to find the best model. You will also be well equipped to calculate feature importance for model evaluation.

The rest of the chapter is locked

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Authors (4)

Blaine Bateman

Blaine Bateman

Blaine Bateman has more than 35 years of experience working with various industries from government R&D to startups to $1B public companies. His experience focuses on analytics including machine learning and forecasting. His hands-on abilities include Python and R coding, Keras/Tensorflow, and AWS & Azure machine learning services. As a machine learning consultant, he has developed and deployed actual ML models in industry.

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Mathur

Mathur

Ishita Mathur has worked as a data scientist for 2.5 years with product-based start-ups working with business concerns in various domains and formulating them as technical problems that can be solved using data and machine learning. Her current work at GO-JEK involves the end-to-end development of machine learning projects, by working as part of a product team on defining, prototyping, and implementing data science models within the product. She completed her masters' degree in high-performance computing with data science at the University of Edinburgh, UK, and her bachelor's degree with honors in physics at St. Stephen's College, Delhi.

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Ashish Ranjan Jha

Ashish Ranjan Jha

Ashish Ranjan Jha received his bachelor's degree in electrical engineering from IIT Roorkee (India), a master's degree in Computer Science from EPFL (Switzerland), and an MBA degree from Quantic School of Business (Washington). He has received a distinction in all 3 of his degrees. He has worked for large technology companies, including Oracle and Sony as well as the more recent tech unicorns such as Revolut, mostly focused on artificial intelligence. He currently works as a machine learning engineer. Ashish has worked on a range of products and projects, from developing an app that uses sensor data to predict the mode of transport to detecting fraud in car damage insurance claims. Besides being an author, machine learning engineer, and data scientist, he also blogs frequently on his personal blog site about the latest research and engineering topics around machine learning.

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Benjamin Johnston

Benjamin Johnston

Benjamin Johnston is a senior data scientist for one of the world's leading data-driven MedTech companies and is involved in the development of innovative digital solutions throughout the entire product development pathway, from problem definition to solution research and development, through to final deployment. He is currently completing his Ph.D. in machine learning, specializing in image processing and deep convolutional neural networks. He has more than 10 years of experience in medical device design and development, working in a variety of technical roles, and holds first-class honors bachelor's degrees in both engineering and medical science from the University of Sydney, Australia.

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