There are two main disadvantages to using decision trees. First, decision trees use algorithms that make a choice to split on an attribute based on a cost function. The decision tree algorithm is a greedy algorithm that optimizes toward a local optimum when making every decision regarding splitting the dataset into two subsets. However, it does not explore whether making a suboptimal decision while splitting over an attribute would lead to a more optimal decision tree in the future. Hence, we do not get a globally optimum tree when running this algorithm. Second, decision trees tend to overfit to the training data. For example, a small sample of observations available in the dataset may lead to a branch that provides a very high probability of a certain class event occurring. This leads to the decision trees being really good at generating...
Mastering Machine Learning on AWS
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Mastering Machine Learning on AWS
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Overview of this book
Amazon Web Services (AWS) is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud.
As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned, and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics and predictive modeling through to sentiment analysis.
By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS.
Table of Contents (24 chapters)
Preface
Getting Started with Machine Learning for AWS
Section 2: Implementing Machine Learning Algorithms at Scale on AWS
Classifying Twitter Feeds with Naive Bayes
Predicting House Value with Regression Algorithms
Predicting User Behavior with Tree-Based Methods
Customer Segmentation Using Clustering Algorithms
Analyzing Visitor Patterns to Make Recommendations
Section 3: Deep Learning
Implementing Deep Learning Algorithms
Implementing Deep Learning with TensorFlow on AWS
Image Classification and Detection with SageMaker
Section 4: Integrating Ready-Made AWS Machine Learning Services
Working with AWS Comprehend
Using AWS Rekognition
Building Conversational Interfaces Using AWS Lex
Section 5: Optimizing and Deploying Models through AWS
Creating Clusters on AWS
Optimizing Models in Spark and SageMaker
Tuning Clusters for Machine Learning
Deploying Models Built in AWS
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Appendix: Getting Started with AWS
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