Practical machine learning cookbook resolving and offering solutions to your machine learning problems with R

Building Machine Learning applications with R About This Book Implement a wide range of algorithms and techniques for tackling complex data Improve predictions and recommendations to have better levels of accuracy Optimize performance of your machine-learning systems Who This Book Is For This book i...

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Detalles Bibliográficos
Otros Autores:	Tripathi, Atul, author (author)
Formato:	Libro electrónico
Idioma:	Inglés
Publicado:	Birmingham, England ; Mumbai, [India] : Packt Publishing 2017.
Edición:	1st edition
Materias:	R (Computer program language)
Ver en Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009630121806719

Tabla de Contenidos:

Cover
Copyright
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Table of Contents
Preface
Chapter 1: Introduction to Machine Learning
What is machine learning?
An overview of classification
An overview of clustering
An overview of supervised learning
An overview of unsupervised learning
An overview of reinforcement learning
An overview of structured prediction
An overview of neural networks
An overview of deep learning
Chapter 2: Classification
Introduction
Discriminant function analysis - geological measurements on brines from wells
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - transforming data
Step 4 - training the model
Step 5 - classifying the data
Step 6 - evaluating the model
Multinomial logistic regression - understanding program choices made by students
Getting ready
Step 1 - collecting data
How to do it...
Step 2 - exploring data
Step 3 - training the model
Step 4 - testing the results of the model
Step 5 - model improvement performance
Tobit regression - measuring the students' academic aptitude
Getting ready
Step 1 - collecting data
How to do it...
Step 2 - exploring data
Step 3 - plotting data
Step 4 - exploring relationships
Step 5 - training the model
Step 6 - testing the model
Poisson regression - understanding species present in Galapagos Islands
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - plotting data and testing empirical data
Step 4 - rectifying discretization of the Poisson model
Step 5 - training and evaluating the model using the link function
Step 6 - revaluating using the Poisson model.
Step 7 - revaluating using the linear model
Chapter 3: Clustering
Introduction
Hierarchical clustering - World Bank sample dataset
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - transforming data
Step 4 - training and evaluating the model performance
Step 5 - plotting the model
Hierarchical clustering - Amazon rainforest burned between 1999-2010
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - transforming data
Step 4 - training and evaluating model performance
Step 5 - plotting the model
Step 6 - improving model performance
Hierarchical clustering - gene clustering
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - transforming data
Step 4 - training the model
Step 5 - plotting the model
Binary clustering - math test
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - training and evaluating model performance
Step 4 - plotting the model
Step 5 - K-medoids clustering
K-means clustering - European countries protein consumption
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - clustering
Step 4 - improving the model
K-means clustering - foodstuff
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - transforming data
Step 4 - clustering
Step 5 - visualizing the clusters
Chapter 4: Model Selection and Regularization
Introduction
Shrinkage methods - calories burned per day
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data.
Step 3 - building the model
Step 4 - improving the model
Step 5 - comparing the model
Dimension reduction methods - Delta's Aircraft Fleet
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - applying principal components analysis
Step 4 - scaling the data
Step 5 - visualizing in 3D plot
Principal component analysis - understanding world cuisine
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - preparing data
Step 4 - applying principal components analysis
Chapter 5: Nonlinearity
Generalized additive models - measuring the household income of New Zealand
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - setting up the data for the model
Step 4 - building the model
Smoothing splines - understanding cars and speed
How to do it...
Step 1 - exploring the data
Step 2 - creating the model
Step 3 - fitting the smooth curve model
Step 4 - plotting the results
Local regression - understanding drought warnings and impact
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - collecting and exploring data
Step 3 - calculating the moving average
Step 4 - calculating percentiles
Step 5 - plotting results
Chapter 6: Supervised Learning
Introduction
Decision tree learning - Advance Health Directive for patients with chest pain
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - preparing the data
Step 4 - training the model
Step 5- improving the model
Decision tree learning - income-based distribution of real estate values
Getting ready.
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - training the model
Step 4 - comparing the predictions
Step 5 - improving the model
Decision tree learning - predicting the direction of stock movement
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - calculating the indicators
Step 4 - preparing variables to build datasets
Step 5 - building the model
Step 6 - improving the model
Naive Bayes - predicting the direction of stock movement
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - preparing variables to build datasets
Step 4 - building the model
Step 5 - creating data for a new, improved model
Step 6 - improving the model
Random forest - currency trading strategy
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - preparing variables to build datasets
Step 4 - building the model
Support vector machine - currency trading strategy
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - calculating the indicators
Step 4 - preparing variables to build datasets
Step 5 - building the model
Stochastic gradient descent - adult income
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - preparing the data
Step 4 - building the model
Step 5 - plotting the model
Chapter 7: Unsupervised Learning
Introduction
Self-organizing map - visualizing of heatmaps
How to do it...
Step 1 - exploring data
Step 2 - training the model
Step 3 - plotting the model.
Vector quantization - image clustering
Getting ready
Step 1 - collecting and describing data
How to do it...
Step 2 - exploring data
Step 3 - data cleaning
Step 4 - visualizing cleaned data
Step 5 - building the model and visualizing it
Chapter 8: Reinforcement Learning
Introduction
Markov chains - the stocks regime switching model
Getting ready
Step 1 - collecting and describing the data
How to do it...
Step 2 - exploring the data
Step 3 - preparing the regression model
Step 4 - preparing the Markov-switching model
Step 5 - plotting the regime probabilities
Step 6 - testing the Markov switching model
Markov chains - the multi-channel attribution model
Getting ready
How to do it...
Step 1 - preparing the dataset
Step 2 - preparing the model
Step 3 - plotting the Markov graph
Step 4 - simulating the dataset of customer journeys
Step 5 - preparing a transition matrix heat map for real data
Markov chains - the car rental agency service
How to do it...
Step 1 - preparing the dataset
Step 2 - preparing the model
Step 3 - improving the model
Continuous Markov chains - vehicle service at a gas station
Getting ready
How to do it...
Step 1 - preparing the dataset
Step 2 - computing the theoretical resolution
Step 3 - verifying the convergence of a theoretical solution
Step 4 - plotting the results
Monte Carlo simulations - calibrated Hull and White short-rates
Getting ready
Step 1 - installing the packages and libraries
How to do it...
Step 2 - initializing the data and variables
Step 3 - pricing the Bermudan swaptions
Step 4 - constructing the spot term structure of interest rates
Step 5 - simulating Hull-White short-rates
Chapter 9: Structured Prediction
Introduction
Hidden Markov models - EUR and USD.
Getting ready.

Practical machine learning cookbook resolving and offering solutions to your machine learning problems with R

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