Machine Learning With R Cookbook - Second Edition · Analyze Data and Build Predictive Models by Bhatia, Ashishsingh -- Read -- Imperial Library of Trantor

Index

Title Page

Second Edition

Machine Learning with R Cookbook

Second Edition

Credits About the Authors About the Reviewers www.PacktPub.com

Why subscribe?

Customer Feedback Preface

What this book covers What you need for this book Who this book is for Sections

Getting ready How to do it… How it works… There's more… See also

Conventions Reader feedback Customer support

Downloading the example code Errata Piracy Questions

Practical Machine Learning with R

Introduction Downloading and installing R

Getting ready How to do it... How it works... See also

Downloading and installing RStudio

Getting ready How to do it... How it works... See also

Installing and loading packages

Getting ready How to do it... How it works... See also

Understanding of basic data structures

Data types Data structures Vectors

How to do it... How it works...

Lists

How to do it... How it works...

Array

How to do it... How it works...

Matrix

How to do it...

DataFrame How to do it...

Basic commands for subsetting

How to do it... Data input

Reading and writing data

Getting ready How to do it... How it works... There's more...

Manipulating data

Getting ready How to do it... How it works... There's more...

Applying basic statistics

Getting ready How to do it... How it works... There's more...

Visualizing data

Getting ready How to do it... How it works... See also

Getting a dataset for machine learning

Getting ready How to do it... How it works... See also

Data Exploration with Air Quality Datasets

Introduction Using air quality dataset

Getting ready How to do it... How it works... There's more...

Converting attributes to factor

Getting ready How to do it... How it works... There's more...

Detecting missing values

Getting ready How to do it... How it works... There's more...

Imputing missing values

Getting ready How to do it... How it works...

Exploring and visualizing data

Getting ready How to do it...

Predicting values from datasets

Getting ready How to do it... How it works...

Analyzing Time Series Data

Introduction Looking at time series data

Getting ready How to do it... How it works... See also

Plotting and forecasting time series data

Getting ready How to do it... How it works... See also

Extracting, subsetting, merging, filling, and padding

Getting ready How to do it... How it works... See also

Successive differences and moving averages

Getting ready How to do it... How it works... See also

Exponential smoothing

Getting ready How to do it... How it works... See also

Plotting the autocorrelation function

Getting ready How to do it... How it works... See also

R and Statistics

Introduction Understanding data sampling in R

Getting ready How to do it... How it works... See also

Operating a probability distribution in R

Getting ready How to do it... How it works... There's more...

Working with univariate descriptive statistics in R

Getting ready How to do it... How it works... There's more...

Performing correlations and multivariate analysis

Getting ready How to do it... How it works... See also

Conducting an exact binomial test

Getting ready How to do it... How it works... See also

Performing a student's t-test

Getting ready How to do it... How it works... See also

Performing the Kolmogorov-Smirnov test

Getting ready How to do it... How it works... See also

Understanding the Wilcoxon Rank Sum and Signed Rank test

Getting ready How to do it... How it works... See also

Working with Pearson's Chi-squared test

Getting ready How to do it... How it works... There's more...

Conducting a one-way ANOVA

Getting ready How to do it... How it works... There's more...

Performing a two-way ANOVA

Getting ready How to do it... How it works... See also

Understanding Regression Analysis

Introduction Different types of regression Fitting a linear regression model with lm

Getting ready How to do it... How it works... There's more...

Summarizing linear model fits

Getting ready How to do it... How it works... See also

Using linear regression to predict unknown values

Getting ready How to do it... How it works... See also

Generating a diagnostic plot of a fitted model

Getting ready How to do it... How it works... There's more...

Fitting multiple regression

Getting ready How to do it... How it works...

Summarizing multiple regression

Getting ready How to do it... How it works... See also

Using multiple regression to predict unknown values

Getting ready How to do it... How it works... See also

Fitting a polynomial regression model with lm

Getting ready How to do it... How it works... There's more...

Fitting a robust linear regression model with rlm

Getting ready How to do it... How it works... There's more...

Studying a case of linear regression on SLID data

Getting ready How to do it... How it works... See also

Applying the Gaussian model for generalized linear regression

Getting ready How to do it... How it works... See also

Applying the Poisson model for generalized linear regression

Getting ready How to do it... How it works... See also

Applying the Binomial model for generalized linear regression

Getting ready How to do it... How it works... See also

Fitting a generalized additive model to data

Getting ready How to do it... How it works... See also

Visualizing a generalized additive model

Getting ready How to do it... How it works... There's more...

Diagnosing a generalized additive model

Getting ready How to do it... How it works... There's more...

Survival Analysis

Introduction Loading and observing data

Getting ready How to do it... How it works... There's more...

Viewing the summary of survival analysis

Getting ready How to do it... How it works...

Visualizing the Survival Curve

Getting ready How to do it... How it works...

Using the log-rank test

Getting ready How to do it... How it works...

Using the COX proportional hazard model

Getting ready How to do it... How it works...

Nelson-Aalen Estimator of cumulative hazard

Getting ready How to do it... How it works... See also

Classification 1 - Tree, Lazy, and Probabilistic

Introduction Preparing the training and testing datasets

Getting ready How to do it... How it works... There's more...

Building a classification model with recursive partitioning trees

Getting ready How to do it... How it works... See also

Visualizing a recursive partitioning tree

Getting ready How to do it... How it works... See also

Measuring the prediction performance of a recursive partitioning tree

Getting ready How to do it... How it works... See also

Pruning a recursive partitioning tree

Getting ready How to do it... How it works... See also

Handling missing data and split and surrogate variables

Getting ready How to do it... How it works... See also

Building a classification model with a conditional inference tree

Getting ready How to do it... How it works... See also

Control parameters in conditional inference trees

Getting ready How to do it... How it works... See also

Visualizing a conditional inference tree

Getting ready How to do it... How it works... See also

Measuring the prediction performance of a conditional inference tree

Getting ready How to do it... How it works... See also

Classifying data with the k-nearest neighbor classifier

Getting ready How to do it... How it works... See also

Classifying data with logistic regression

Getting ready How to do it... How it works... See also

Classifying data with the Naïve Bayes classifier

Getting ready How to do it... How it works... See also

Classification 2 - Neural Network and SVM

Introduction Classifying data with a support vector machine

Getting ready How to do it... How it works... See also

Choosing the cost of a support vector machine

Getting ready How to do it... How it works... See also

Visualizing an SVM fit

Getting ready How to do it... How it works... See also

Predicting labels based on a model trained by a support vector machine

Getting ready How to do it... How it works... There's more...

Tuning a support vector machine

Getting ready How to do it... How it works... See also

The basics of neural network

Getting ready How to do it...

Training a neural network with neuralnet

Getting ready How to do it... How it works... See also

Visualizing a neural network trained by neuralnet

Getting ready How to do it... How it works... See also

Predicting labels based on a model trained by neuralnet

Getting ready How to do it... How it works... See also

Training a neural network with nnet

Getting ready How to do it... How it works... See also

Predicting labels based on a model trained by nnet

Getting ready How to do it... How it works... See also

Model Evaluation

Introduction

Why do models need to be evaluated? Different methods of model evaluation

Estimating model performance with k-fold cross-validation

Getting ready How to do it... How it works... There's more...

Estimating model performance with Leave One Out Cross Validation

Getting ready How to do it... How it works... See also

Performing cross-validation with the e1071 package

Getting ready How to do it... How it works... See also

Performing cross-validation with the caret package

Getting ready How to do it... How it works... See also

Ranking the variable importance with the caret package

Getting ready How to do it... How it works... There's more...

Ranking the variable importance with the rminer package

Getting ready How to do it... How it works... See also

Finding highly correlated features with the caret package

Getting ready How to do it... How it works... See also

Selecting features using the caret package

Getting ready How to do it... How it works... See also

Measuring the performance of the regression model

Getting ready How to do it... How it works... There's more...

Measuring prediction performance with a confusion matrix

Getting ready How to do it... How it works... See also

Measuring prediction performance using ROCR

Getting ready How to do it... How it works... See also

Comparing an ROC curve using the caret package

Getting ready How to do it... How it works... See also

Measuring performance differences between models with the caret package

Getting ready How to do it... How it works... See also

Ensemble Learning

Introduction Using the Super Learner algorithm

Getting ready How to do it... How it works...

Using ensemble to train and test

Getting ready How to do it... How it works...

Classifying data with the bagging method

Getting ready How to do it... How it works... There's more...

Performing cross-validation with the bagging method

Getting ready How to do it... How it works... See also

Classifying data with the boosting method

Getting ready How to do it... How it works... There's more...

Performing cross-validation with the boosting method

Getting ready How to do it... How it works... See also

Classifying data with gradient boosting

Getting ready How to do it... How it works... There's more...

Calculating the margins of a classifier

Getting ready How to do it... How it works... See also

Calculating the error evolution of the ensemble method

Getting ready How to do it... How it works... See also

Classifying data with random forest

Getting ready How to do it... How it works... There's more...

Estimating the prediction errors of different classifiers

Getting ready How to do it... How it works... See also

Clustering

Introduction Clustering data with hierarchical clustering

Getting ready How to do it... How it works... There's more...

Cutting trees into clusters

Getting ready How to do it... How it works... There's more...

Clustering data with the k-means method

Getting ready How to do it... How it works... See also

Drawing a bivariate cluster plot

Getting ready How to do it... How it works... There's more...

Comparing clustering methods

Getting ready How to do it... How it works... See also

Extracting silhouette information from clustering

Getting ready How to do it... How it works... See also

Obtaining the optimum number of clusters for k-means

Getting ready How to do it... How it works... See also

Clustering data with the density-based method

Getting ready How to do it... How it works... See also

Clustering data with the model-based method

Getting ready How to do it... How it works... See also

Visualizing a dissimilarity matrix

Getting ready How to do it... How it works... There's more...

Validating clusters externally

Getting ready How to do it... How it works... See also

Association Analysis and Sequence Mining

Introduction Transforming data into transactions

Getting ready How to do it... How it works... See also

Displaying transactions and associations

Getting ready How to do it... How it works... See also

Mining associations with the Apriori rule

Getting ready How to do it... How it works... See also

Pruning redundant rules

Getting ready How to do it... How it works... See also

Visualizing association rules

Getting ready How to do it... How it works... See also

Mining frequent itemsets with Eclat

Getting ready How to do it... How it works... See also

Creating transactions with temporal information

Getting ready How to do it... How it works... See also

Mining frequent sequential patterns with cSPADE

Getting ready How to do it... How it works... See also

Using the TraMineR package for sequence analysis

Getting ready How to do it... How it works...

Visualizing sequence, Chronogram, and Traversal Statistics

Getting ready How to do it... How it works... See also

Dimension Reduction

Introduction Why to reduce the dimension? Performing feature selection with FSelector

Getting ready How to do it... How it works... See also

Performing dimension reduction with PCA

Getting ready How to do it... How it works... There's more...

Determining the number of principal components using the scree test

Getting ready How to do it... How it works... There's more...

Determining the number of principal components using the Kaiser method

Getting ready How to do it... How it works... See also

Visualizing multivariate data using biplot

Getting ready How to do it... How it works... There's more...

Performing dimension reduction with MDS

Getting ready How to do it... How it works... There's more...

Reducing dimensions with SVD

Getting ready How to do it... How it works... See also

Compressing images with SVD

Getting ready How to do it... How it works... See also

Performing nonlinear dimension reduction with ISOMAP

Getting ready How to do it... How it works... There's more...

Performing nonlinear dimension reduction with Local Linear Embedding

Getting ready How to do it... How it works... See also

Big Data Analysis (R and Hadoop)

Introduction Preparing the RHadoop environment

Getting ready How to do it... How it works... See also

Installing rmr2

Getting ready How to do it... How it works... See also

Installing rhdfs

Getting ready How to do it... How it works... See also

Operating HDFS with rhdfs

Getting ready How to do it... How it works... See also

Implementing a word count problem with RHadoop

Getting ready How to do it... How it works... See also

Comparing the performance between an R MapReduce program and a standard R program

Getting ready How to do it... How it works... See also

Testing and debugging the rmr2 program

Getting ready How to do it... How it works... See also

Installing plyrmr

Getting ready How to do it... How it works... See also

Manipulating data with plyrmr

Getting ready How to do it... How it works... See also

Conducting machine learning with RHadoop

Getting ready How to do it... How it works... See also

Configuring RHadoop clusters on Amazon EMR

Getting ready How to do it... How it works... See also

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