Book Image

Effective Amazon Machine Learning

By : Alexis Perrier
Book Image

Effective Amazon Machine Learning

By: Alexis Perrier

Overview of this book

Predictive analytics is a complex domain requiring coding skills, an understanding of the mathematical concepts underpinning machine learning algorithms, and the ability to create compelling data visualizations. Following AWS simplifying Machine learning, this book will help you bring predictive analytics projects to fruition in three easy steps: data preparation, model tuning, and model selection. This book will introduce you to the Amazon Machine Learning platform and will implement core data science concepts such as classification, regression, regularization, overfitting, model selection, and evaluation. Furthermore, you will learn to leverage the Amazon Web Service (AWS) ecosystem for extended access to data sources, implement realtime predictions, and run Amazon Machine Learning projects via the command line and the Python SDK. Towards the end of the book, you will also learn how to apply these services to other problems, such as text mining, and to more complex datasets.
Table of Contents (17 chapters)
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Dedication
Preface

Analyzing the logs


For every operation it carries out, Amazon ML gives us access to the related logs. We can download and analyze the model training logs and infer a few things on how Amazon ML trains and selects the best model.

Go back to the last Titanic model, and in the summary part, click on the Download Log link. The log file is too long to be reproduced here but is available at https://github.com/alexperrier/packt-aml/blob/master/ch5/titanic_training.log:

Amazon ML launches five versions of the SGD algorithm in parallel. Each version is called a learner and corresponds to a different value for the learning rate: 0.01, 0.1,1, 10, and 100. The following five metrics are calculated at each new pass of the algorithm:

  • Accuracy
  • Recall
  • Precision
  • F1-score
  • AUC

The negative-log-likelihood is also calculated to assess whether the last iterations have brought significant improvement in reducing the residual error.

Optimizing the learning rate

If you recall from Chapter 2Machine Learning Definitions and...