Book Image

Mastering Java Machine Learning

By : Uday Kamath, Krishna Choppella
Book Image

Mastering Java Machine Learning

By: Uday Kamath, Krishna Choppella

Overview of this book

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.
Table of Contents (20 chapters)
Mastering Java Machine Learning
Credits
Foreword
About the Authors
About the Reviewers
www.PacktPub.com
Customer Feedback
Preface
Linear Algebra
Index

Model assessment, evaluation, and comparisons


The key ideas discussed here are:

  • How to assess or estimate the performance of the classifier on unseen datasets that it will be predicting on future unseen datasets.

  • What are the metrics that we should use to assess the performance of the model?

  • How do we compare algorithms if we have to choose between them?

Model assessment

In order to train the model(s), tune the model parameters, select the models, and finally estimate the predictive behavior of models on unseen data, we need many datasets. We cannot train the model on one set of data and estimate its behavior on the same set of data, as it will have a clear optimistic bias and estimations will be unlikely to match the behavior in the unseen data. So at a minimum, there is a need to partition data available into training sets and testing sets. Also, we need to tune the parameters of the model and test the effect of the tuning on a separate dataset before we perform testing on the test set. The...