Book Image

Hands-On Automated Machine Learning

By : Sibanjan Das, Umit Mert Cakmak
Book Image

Hands-On Automated Machine Learning

By: Sibanjan Das, Umit Mert Cakmak

Overview of this book

AutoML is designed to automate parts of Machine Learning. Readily available AutoML tools are making data science practitioners’ work easy and are received well in the advanced analytics community. Automated Machine Learning covers the necessary foundation needed to create automated machine learning modules and helps you get up to speed with them in the most practical way possible. In this book, you’ll learn how to automate different tasks in the machine learning pipeline such as data preprocessing, feature selection, model training, model optimization, and much more. In addition to this, it demonstrates how you can use the available automation libraries, such as auto-sklearn and MLBox, and create and extend your own custom AutoML components for Machine Learning. By the end of this book, you will have a clearer understanding of the different aspects of automated Machine Learning, and you’ll be able to incorporate automation tasks using practical datasets. You can leverage your learning from this book to implement Machine Learning in your projects and get a step closer to winning various machine learning competitions.

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Table of Contents (10 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Introduction to AutoML
Introduction to AutoML
Scope of machine learning
What is AutoML?
Why use AutoML and how does it help?
When do you automate ML?
What will you learn?
Overview of AutoML libraries
Summary
2
Introduction to Machine Learning Using Python
Introduction to Machine Learning Using Python
Technical requirements
Machine learning
Linear regression
Important evaluation metrics – regression algorithms
Logistic regression
Important evaluation metrics – classification algorithms
Decision trees
Support Vector Machines
k-Nearest Neighbors
Ensemble methods
Comparing the results of classifiers
Cross-validation
Clustering
Summary
3
Data Preprocessing
Data Preprocessing
Technical requirements
Data transformation
Feature selection
Feature generation
Summary
4
Automated Algorithm Selection
Automated Algorithm Selection
Technical requirements
Computational complexity
Differences in training and scoring time
Linearity versus non-linearity
Necessary feature transformations
Supervised ML
Unsupervised AutoML
Summary
5
Hyperparameter Optimization
Hyperparameter Optimization
Technical requirements
Hyperparameters
Warm start
Bayesian-based hyperparameter tuning
An example system
Summary
6
Creating AutoML Pipelines
Creating AutoML Pipelines
Technical requirements
An introduction to machine learning pipelines
A simple pipeline
FunctionTransformer
A complex pipeline
Summary
7
Dive into Deep Learning
Dive into Deep Learning
Technical requirements
Overview of neural networks
A feed-forward neural network using Keras
Autoencoders
Convolutional Neural Networks
Summary
8
Critical Aspects of ML and Data Science Projects
Critical Aspects of ML and Data Science Projects
Machine learning as a search
Trade-offs in machine learning
Engagement model for a typical data science project
The phases of an engagement model
Summary
9
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Important evaluation metrics – regression algorithms

Assessing the value of a ML model is a two-phase process. First, the model has to be evaluated for its statistical accuracy, that is, whether the statistical hypotheses are correct, model performance is outstanding, and the performance holds true for other independent datasets. This is accomplished using several model evaluation metrics. Then, a model is evaluated to see if the results are as expected as per business requirement and the stakeholders genuinely get some insights or useful predictions out of it.

A regression model is evaluated based on the following metrics:

  • Mean absolute error (MAE): It is the sum of absolute values of prediction error. The prediction error is defined as the difference between predicted and actual values. This metric gives an idea about the magnitude of the error. However, we cannot judge...
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