book

Microsoft Azure Machine Learning

Name: Microsoft Azure Machine Learning
Author: Sumit Mund
ISBN: 9781784390792

by Sumit Mund

June 2015

Intermediate to advanced

212 pages

4h 25m

English

Packt Publishing

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Microsoft Azure Machine Learning
Table of Contents
Microsoft Azure Machine Learning
Credits
About the Author
Acknowledgments
About the Reviewers
www.PacktPub.com
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Preface
What this book covers
What you need for this book

Who this book is for
Conventions
Reader feedback
Customer support
Downloading the color images of this bookErrataPiracyQuestions
1. Introduction
Introduction to predictive analyticsProblem definition and scopingData collectionData exploration and preparationModel developmentModel deployment
Machine learning
Types of machine learning problemsClassificationRegressionClusteringCommon machine learning techniques/algorithmsLinear regressionLogistic regressionDecision tree-based ensemble modelsNeural networks and deep learning
Introduction to Azure Machine Learning
ML Studio
Summary
2. ML Studio Inside Out
Introduction to ML Studio
Getting started with Microsoft Azure
Microsoft account and subscriptionCreating and managing ML workspaces
Inside ML Studio
ExperimentsCreating and editing an experimentRunning an experimentCreating and running an experiment – do it yourself
Workspace as a collaborative environment
Summary
3. Data Exploration and Visualization
The basic conceptsThe meanThe medianStandard deviation and varianceUnderstanding a histogramThe box and whiskers plotThe outliersA scatter plot
Data exploration in ML Studio
Visualizing an automobile price datasetA histogramThe box and whiskers plotComparing featuresA snapshot
Do it yourself
Summary
4. Getting Data in and out of ML Studio
Getting data in ML StudioUploading data from a PCThe Enter Data moduleThe Data Reader moduleGetting data from the WebFetching a public dataset – do it yourselfGetting data from Azure
Data format conversion
Getting data from ML Studio
Saving a dataset on a PCSaving results in ML StudioThe Writer module
Summary
5. Data Preparation
Data manipulationClean Missing DataRemoving duplicate rowsProject columnsThe Metadata Editor moduleThe Add Columns moduleThe Add Rows moduleThe Join module
Splitting data
Do it yourself
The Apply SQL Transformation module
Advanced data preprocessing
Removing outliersData normalizationThe Apply Math Operation moduleFeature selectionThe Filter Based Feature Selection moduleThe Fisher Linear Discriminant Analysis moduleData preparation beyond ready-made modules
Summary
6. Regression Models
Understanding regression algorithms
Train, score, and evaluate
The test and train datasetEvaluatingThe mean absolute errorThe root mean squared errorThe relative absolute errorThe relative squared errorThe coefficient of determination
Linear regression
Optimizing parameters for a learner – the sweep parameters module
The decision forest regression
The train neural network regression – do it yourself
Comparing models with the evaluate model
Comparing models – the neural network and boosted decision tree
Other regression algorithms
No free lunch
Summary
7. Classification Models
Understanding classificationEvaluation metricsTrue positiveFalse positiveTrue negativeFalse negativeAccuracyPrecisionRecallThe F1 scoreThresholdUnderstanding ROC and AUCMotivation for the matrix to consider
Training, scoring, and evaluating modules
Classifying diabetes or not
Two-class bayes point machineTwo-class neural network with parameter sweeping
Predicting adult income with decision-tree-based models
Do it yourself – comparing models to choose the best
Multiclass classification
Evaluation metrics – multiclass classification
Multiclass classification with the Iris dataset
Multiclass decision forestComparing models – multiclass decision forest and logistic regression
Multiclass classification with the Wine dataset
Multiclass neural network with parameter sweepDo it yourself – multiclass decision jungle
Summary
8. Clustering
Understanding the K-means clustering algorithm
Creating a K-means clustering model using ML Studio
Do it yourself
Clustering versus classification
Summary
9. A Recommender System
The Matchbox recommenderTypes of recommendationsUnderstanding the recommender modulesThe Train Matchbox recommenderThe number of traitsThe number of recommendation algorithm iterationsThe Score Matchbox recommenderThe evaluate recommender
Building a recommendation system
Summary
10. Extensibility with R and Python
Introduction to R
Introduction to Python
Why should you extend through R/Python code?
Extending experiments using the Python language
Understanding the Execute Python Script moduleCreating visualizations using PythonA simple time series analysis with the Python scriptImporting the existing Python codeDo it yourself – Python
Extending experiments using the R language
Understanding the Execute R Script moduleA simple time series analysis with the R scriptImporting an existing R codeIncluding an R packageUnderstanding the Create R Model moduleDo it yourself – R
Summary
11. Publishing a Model as a Web Service
Preparing an experiment to be publishedSaving a trained model
Creating a scoring experiment
Specifying the input and output of the web service
Publishing a model as a web service
Visually testing a web service
Consuming a published web service
Web service configuration
Updating the web service
Summary
12. Case Study Exercise I
Problem definition and scope
The dataset
Data exploration and preparation
Feature selection
Model development
Model deployment
Summary
13. Case Study Exercise II
Problem definition and scope
The dataset
Data exploration and preparation
Model development
Model deployment
Summary
Index

Content preview from Microsoft Azure Machine Learning

Chapter 7. Classification Models

Classification is another kind of supervised machine learning. In this chapter, before getting into the details of building a classification model using ML Studio, you will start with gaining the basic knowledge about a classification algorithm and how a model is evaluated. Then, you will build models with different datasets using different algorithms.

Understanding classification

Consider you are given the following hypothetical dataset containing data of patients: the size of the tumor in their body, their age, and a class that justifies whether they are affected by cancer or not, 1 being positive (affected by cancer) and 0 being negative (not affected by cancer):

Age	Tumor size	Class
22	135	0
37	121

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Microsoft Azure Machine Learning

by Sumit Mund

Chapter 7. Classification Models

Understanding classification

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