Index

A

• Add Columns module / The Add Columns module
• Add Rows module / The Add Rows module
• adult income
  • predicting, decision-tree-based models used / Predicting adult income with decision-tree-based models
• Africa Soil Property Prediction Challenge / Problem definition and scope
• Apply Math Operation module / The Apply Math Operation module
• Apply SQL Transformation module
  • about / The Apply SQL Transformation module
• Area Under the Curve (AUC) / Understanding ROC and AUC
• automobile price dataset
  • visualizing / Visualizing an automobile price dataset
  • histogram / A histogram
  • whiskers plot / The box and whiskers plot
  • box plot / The box and whiskers plot
  • features, comparing / Comparing features
  • graph, snapshot / A snapshot
• Azure Machine Learning
  • about / Introduction to Azure Machine Learning
  • ML studio / ML Studio

B

• Bayesian / The Matchbox recommender
• Bayesian Linear Regression Model / Other regression algorithms
• boosted decision tree
  • and neural network, comparing / Comparing models – the neural network and boosted decision tree
• box plot
  • about / The box and whiskers plot

C

• classification
  • about / Understanding classification
  • evaluation metrics / Evaluation metrics
  • versus clustering / Clustering versus classification
• classification issue, machine learning / Classification
• clustering
  • versus classification / Clustering versus classification
• clustering issue, machine learning / Clustering
• Create R Model module
  • about / Understanding the Create R Model module

D

• data
  • splitting / Splitting data
  • normalizing / Data normalization
  • preparing / Data preparation beyond ready-made modules, Data exploration and preparation, Data exploration and preparation
  • exploring / Data exploration and preparation, Data exploration and preparation
  • feature, selecting / Feature selection
• data, manipulating
  • about / Data manipulation, Clean Missing Data
  • duplicate rows, removing / Removing duplicate rows
  • project columns / Project columns
  • Metadata Editor module / The Metadata Editor module
  • Add Columns module / The Add Columns module
  • Add Rows module / The Add Rows module
  • Join module / The Join module
• data, ML Studio
  • uploading, from PC / Uploading data from a PC
  • obtaining, from Web / Getting data from the Web
  • public dataset, fetching / Fetching a public dataset – do it yourself
  • obtaining, from Azure / Getting data from Azure
  • format conversion / Data format conversion
  • obtaining / Getting data from ML Studio
• data, processing
  • about / Advanced data preprocessing
  • outliers, removing / Removing outliers
  • data, normalizing / Data normalization
  • Apply Math Operation module / The Apply Math Operation module
  • feature, selecting / Feature selection
  • Filter Based Feature Selection module / The Filter Based Feature Selection module
  • Fisher Linear Discriminant Analysis module / The Fisher Linear Discriminant Analysis module
• data, splitting
  • Recommender Split / Splitting data
  • Regular Expression / Splitting data
  • Relative Expression / Splitting data
• Data Reader module, ML Studio / The Data Reader module
• dataset
  • about / The basic concepts, The dataset
  • URL / Linear regression, The dataset
  • columns / The dataset
  • PIDN / The dataset
  • SOC / The dataset
  • pH / The dataset
  • Ca / The dataset
  • P / The dataset
  • Sand / The dataset
  • m7497.96 - m599.76 / The dataset
  • soil, depth / The dataset
  • Black Sky Albedo (BSA) / The dataset
  • Compound Topographic Index (CTI) / The dataset
  • ELEV / The dataset
  • EVI / The dataset
  • Land Surface Temperatures (LST) / The dataset
  • Ref / The dataset
  • Reli / The dataset
  • TMAP and TMFI / The dataset
• decision-tree-based models used
  • adult income, predicting / Predicting adult income with decision-tree-based models
• decision forest regression
  • about / The decision forest regression
• decision tree-based ensemble models / Decision tree-based ensemble models
• Deep learning algorithms / Neural networks and deep learning
• determination
  • coefficient / The coefficient of determination
• diabetes
  • classifying / Classifying diabetes or not

E

• Energy Efficiency Regression data module / Do it yourself
• Enter Data module, ML Studio / The Enter Data module
• ETL (Extract, Transform, and Load) / Data collection
• evaluate algorithm
  • about / Train, score, and evaluate
• evaluate model
  • about / Comparing models with the evaluate model
• evaluate recommender
  • about / The evaluate recommender
• evaluation metrics, classification / Evaluation metrics
  • true positive / True positive
  • false positive / False positive
  • true negative / True negative
  • false negative / False negative
  • accuracy / Accuracy
  • precision / Precision
  • recall / Recall
  • F1 score / The F1 score
  • threshold / Threshold
  • receiver operating characteristics (ROC) graph / Understanding ROC and AUC
  • Area Under the Curve (AUC) / Understanding ROC and AUC
  • matric / Motivation for the matrix to consider
• Execute Python Script
  • about / Extending experiments using the Python language
• Execute Python Script module
  • about / Understanding the Execute Python Script module
• Execute R Script module
  • about / Understanding the Execute R Script module
• experiment
  • preparing, to publish / Preparing an experiment to be published

F

• F1 score / The F1 score
• Filter Based Feature Selection module / The Filter Based Feature Selection module
• Fisher Linear Discriminant Analysis module / The Fisher Linear Discriminant Analysis module
• Full Outer Join / The Join module

H

• histogram
  • about / Understanding a histogram

I

• Inner Join / The Join module
• Iris dataset
  • multiclass classification with / Multiclass classification with the Iris dataset
  • URL / Multiclass classification with the Iris dataset, Multiclass decision forest
• issue
  • scope / Problem definition and scope

J

• Join module
  • about / The Join module
  • Inner Join / The Join module
  • Left Outer Join / The Join module
  • Full Outer Join / The Join module
  • Left Semi-Join / The Join module

K

• K-means clustering algorithm
  • about / Understanding the K-means clustering algorithm
  • ML Studio used / Creating a K-means clustering model using ML Studio
• Kaggle
  • URL / Problem definition and scope

L

• Left Outer Join / The Join module
• Left Semi-Join / The Join module
• linear regression / Linear regression
  • about / Linear regression
• logistic regression / Logistic regression

M

• machine learning
  • about / Machine learning
• machine learning, issues
  • about / Types of machine learning problems
  • classification / Classification
  • regression / Regression
  • clustering / Clustering
• machine learning, technique/algorithms
  • about / Common machine learning techniques/algorithms
  • lnear regression / Linear regression
  • logistic regression / Logistic regression
  • decision tree-based ensemble models / Decision tree-based ensemble models
  • neural networks algorithms / Neural networks and deep learning
• Matchbox recommender
  • about / The Matchbox recommender
  • URL / The Matchbox recommender
  • Rating Prediction / Types of recommendations
  • Item Recommendation / Types of recommendations
  • Related Users / Types of recommendations
  • Related Items / Types of recommendations
  • modules / Understanding the recommender modules
  • train matchbox recommender / The Train Matchbox recommender
  • score matchbox recommender / The Score Matchbox recommender
  • evaluate recommender / The evaluate recommender
  • recommendation system, building / Building a recommendation system
• mean
  • about / The mean
• mean absolute error (MAE)
  • about / The mean absolute error
• median
  • about / The median
• Metadata Editor module / The Metadata Editor module
• Microsoft Azure
  • about / Getting started with Microsoft Azure
  • Microsoft account, creating / Microsoft account and subscription
  • Microsoft account subscription / Microsoft account and subscription
  • ML workspaces, creating / Creating and managing ML workspaces
  • ML workspaces, managing / Creating and managing ML workspaces
• ML Studio
  • about / Introduction to ML Studio
  • R or Python code / Introduction to ML Studio
  • URL / Creating and managing ML workspaces
  • home page / Inside ML Studio
  • experiment / Experiments
  • experiment, creating / Creating and editing an experiment
  • experiment, editing / Creating and editing an experiment
  • experiment, running / Running an experiment
  • simple experiment, creating / Creating and running an experiment – do it yourself
  • simple experiment, running / Creating and running an experiment – do it yourself
  • data, exploring / Data exploration in ML Studio
  • data, getting / Getting data in ML Studio
  • data, uploading from PC / Uploading data from a PC
  • Enter Data module / The Enter Data module
  • Data Reader module / The Data Reader module
  • data, obtaining from Web / Getting data from the Web
  • public dataset, fetching / Fetching a public dataset – do it yourself
  • data, obtaining from Azure / Getting data from Azure
  • data format conversions / Data format conversion
  • data, obtaining from / Getting data from ML Studio
  • dataset, saving in PC / Saving a dataset on a PC
  • results, saving / Saving results in ML Studio
  • Writer module / The Writer module
  • used, for K-means clustering algorithm / Creating a K-means clustering model using ML Studio
• model
  • publishing, as web service / Publishing a model as a web service
  • developing / Model development, Model development
  • deploying / Model deployment, Model deployment
• models
  • comparing, with evaluate model / Comparing models with the evaluate model
  • comparing / Do it yourself – comparing models to choose the best
• modules
  • training / Training, scoring, and evaluating modules
  • scoring / Training, scoring, and evaluating modules
  • evaluating / Training, scoring, and evaluating modules
• multiclass classification
  • about / Multiclass classification
  • evaluation metrics / Evaluation metrics – multiclass classification
  • with Iris dataset / Multiclass classification with the Iris dataset
  • with Wine dataset / Multiclass classification with the Wine dataset
• multiclass classification, with Iris dataset
  • about / Multiclass classification with the Iris dataset
  • multiclass decision forest / Multiclass decision forest
  • models, comparing / Comparing models – multiclass decision forest and logistic regression
• multiclass classification, with Wine dataset
  • about / Multiclass classification with the Wine dataset
  • multiclass neural network, with parameter sweep / Multiclass neural network with parameter sweep
• Multiple Imputation by Chained Equations (MICE) / Clean Missing Data

N

• neural network / Neural networks and deep learning
  • and boosted decision tree, comparing / Comparing models – the neural network and boosted decision tree
• No free lunch theorem / No free lunch

O

• Ordinal Regression Model / Other regression algorithms
• outliers
  • about / The outliers
  • removing / Removing outliers

P

• parameters
  • optimizing, for learner / Optimizing parameters for a learner – the sweep parameters module
• parameter sweeping
  • Two-class neural network / Two-class neural network with parameter sweeping
  • multiclass neural network with / Multiclass neural network with parameter sweep
• predictive analytics
  • about / Introduction to predictive analytics
  • issue, defining / Problem definition and scoping
  • issue, scope / Problem definition and scoping
  • data, collecting / Data collection
  • data, exploring / Data exploration and preparation
  • model, developing / Model development
  • model, deploying / Model deployment
• Principal Component Analysis (PCA) / Clean Missing Data, Data exploration and preparation
• Python
  • about / Introduction to Python
  • used, for creating visualizations / Creating visualizations using Python
• Python code
  • extending / Why should you extend through R/Python code?
  • existing code, importing / Importing the existing Python code
• Python script
  • time series analysis with / A simple time series analysis with the Python script

R

• R
  • about / Introduction to R
  • extending / Why should you extend through R/Python code?
  • used, for extending experiments / Extending experiments using the R language
  • Execute R Script module / Understanding the Execute R Script module
  • time series analysis / A simple time series analysis with the R script
• R code
  • importing / Importing an existing R code
• receiver operating characteristics (ROC) graph / Understanding ROC and AUC
• regression algorithms
  • about / Understanding regression algorithms
  • Bayesian Linear Regression Model / Other regression algorithms
  • Ordinal Regression Model / Other regression algorithms
  • Poisson Regression / Other regression algorithms
• regression issue, machine learning / Regression
• relative absolute error (RAE)
  • about / The relative absolute error
• relative squared error (RSE)
  • about / The relative squared error
• root mean squared error (RMSE)
  • about / The root mean squared error
• R package
  • including / Including an R package

S

• scatter plot
  • about / A scatter plot
• score algorithm
  • about / Train, score, and evaluate
• score matchbox recommender
  • about / The Score Matchbox recommender
• scoring experiment
  • creating / Creating a scoring experiment
• SQL Azure Tables / Getting data from Azure
• standard deviation
  • about / Standard deviation and variance
• Supervised Machine Learning / Machine learning
• sweep parameters module
  • about / Optimizing parameters for a learner – the sweep parameters module

T

• test dataset
  • about / The test and train dataset, Evaluating
• threshold / Threshold
• time series analysis
  • with Python script / A simple time series analysis with the Python script
  • with R / A simple time series analysis with the R script
  • Create R Model module / Understanding the Create R Model module
• Time Series Dataset
  • about / Extending experiments using the Python language
• train dataset
  • about / The test and train dataset, Evaluating
• trained algorithm
  • about / Train, score, and evaluate
• trained model
  • saving / Saving a trained model
• train matchbox recommender
  • about / The Train Matchbox recommender
  • number of traits / The number of traits
  • number of recommendation algorithm iterations / The number of recommendation algorithm iterations
• train neural network regression
  • about / The train neural network regression – do it yourself
• TranStats data collection
  • URL / The dataset
• Two-class bayes point machine / Two-class bayes point machine
• Two-class neural network
  • with parameter sweeping / Two-class neural network with parameter sweeping

U

• unsupervised machine learning
  • about / Machine learning

V

• variance
  • about / Standard deviation and variance
• visualizations
  • creating, Python used / Creating visualizations using Python

W

• web service
  • input, specifying / Specifying the input and output of the web service
  • output, specifying / Specifying the input and output of the web service
  • model, publishing as / Publishing a model as a web service
  • testing, visually / Visually testing a web service
  • published web service, consuming / Consuming a published web service
  • configuring / Web service configuration
  • updating / Updating the web service
• whiskers plot
  • about / The box and whiskers plot
• Windows Azure BLOB Storage / Getting data from Azure
• Windows Azure Table Storage / Getting data from Azure
• Wine dataset
  • multiclass classification with / Multiclass classification with the Wine dataset
  • URL / Multiclass classification with the Wine dataset, Multiclass neural network with parameter sweep
• workspace
  • as collaborative environment / Workspace as a collaborative environment
• Writer module / The Writer module