Model-Free Prediction and Regression

Cover --
Copyright --
Credits --
About the Author --
Acknowledgments --
About the Reviewers --
www.PacktPub.com --
Table of Contents --
Preface --
Chapter 1: Introduction --
Introduction to predictive analytics --
Problem definition and scoping --
Data collection --
Data exploration and preparation --
Model development --
Model deployment --
Machine learning --
Kinds of machine learning problems --
Classification --
Regression --
Clustering --
Common machine learning techniques/algorithms --
Linear regression --
Logistic regression --
Decision tree-based ensemble models --
Neural networks and deep learning --
Introduction to Azure Machine Learning --
ML Studio --
Summary --
Chapter 2: ML Studio Inside Out --
Introduction to ML Studio --
Getting started with Microsoft Azure --
Microsoft account and subscription --
Creating and managing ML workspaces --
Inside ML Studio --
Experiments --
Creating and editing an experiment --
Running an experiment --
Creating and running an experiment --
do it yourself --
Workspace as a collaborative environment --
Summary --
Chapter 3: Data Exploration and Visualization --
The basic concepts --
The mean --
The median --
Standard deviation and variance --
Understanding a histogram --
The box and whiskers plot --
The outliers --
A scatter plot --
Data exploration in ML Studio --
Visualizing an automobile price dataset --
A histogram --
The box and whiskers plot --
Comparing features --
A snapshot --
Do it yourself --
Summary --
Chapter 4: Getting Data in and out of ML Studio --
Getting data in ML Studio --
Uploading data from a PC --
The Enter Data module --
The Data Reader module --
Getting data from the Web --
Getting data from Azure --
Data format conversion --
Getting data from ML Studio --
Saving dataset in a PC --
Saving results in ML Studio --
The Writer module --
Summary --
Chapter 5: Data Preparation. Data manipulation --
Clean Missing Data --
Removing duplicate rows --
Project columns --
The Metadata Editor module --
The Add Columns module --
The Add Rows module --
The Join module --
Splitting data --
Do it yourself --
The Apply SQL Transformation module --
Advanced data preprocessing --
Removing outliers --
Data normalization --
The Apply Math Operation module --
Feature selection --
The Filter Based Feature Selection module --
The Fisher Linear Discriminant Analysis module --
Data preparation beyond ready-made modules --
Summary --
Chapter 6: Regression Models --
Understanding regression algorithms --
Train, score, and evaluate --
The test and train dataset --
Evaluating --
The mean absolute error --
The root mean squared error --
The relative absolute error --
The relative squared error --
The 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 --
Chapter 7: Classification Models --
Understanding classification --
Evaluation metrics --
True positive --
False positive --
True negative --
False negative --
Accuracy --
Precision --
Recall --
The F1 score --
Threshold --
Understanding ROC and AUC --
Motivation for the matrix to consider --
Training, scoring, and evaluating modules --
Classifying diabetes or not --
Two-class bayes point machine --
Two-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 forest --
Comparing models --
multiclass decision forest and logistic regression --
Multiclass classification with the Wine dataset --
Multiclass neural network with parameter sweep --
Do it yourself --
multiclass decision jungle --
Summary --
Chapter 8: Clustering --
Understanding the K-means clustering algorithm --
Creating a K-means clustering model using ML Studio --
Do it yourself --
Clustering versus classification --
Summary --
Chapter 9: A Recommender System --
The Matchbox recommender --
Kinds of recommendations --
Understanding the recommender modules --
The train Matchbox recommender --
The score matchbox recommender --
The evaluate recommender --
Building a recommendation system --
Summary --
Chapter 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 module --
Creating visualizations using Python --
A simple time series analysis with the Python script --
Importing the existing Python code --
Do it yourself --
Python --
Extending experiments using the R language --
Understanding the Execute R Script module --
A simple time series analysis with the R script --
Importing an existing R code --
Including an R package --
Understanding the Create R Model module --
Do it yourself --
R --
Summary --
Chapter 11: Publishing a Model as a Web Service --
Preparing an experiment to be published --
Saving 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 --
Chapter 12: Case Study Exercise I. Problem definition and scope --
The dataset --
Data exploration and preparation --
Feature selection --
Model development --
Model deployment --
Summary --
Chapter 13: Case Study Exercise II --
Problem definition and scope --
The dataset --
Data exploration and preparation --
Model development --
Model deployment --
Summary --
Index.