R Machine Learning solutions [Video]

Book Image

R Machine Learning solutions [Video]

By : Yu-Wei, Chiu (David Chiu)

Book Image

R Machine Learning solutions [Video]

By: Yu-Wei, Chiu (David Chiu)

Overview of this book

<p>R is a statistical programming language that provides impressive tools to analyze data and create high-level graphics. This video course will take you from very basics of R to creating insightful machine learning models with R. You will start with setting up the environment and then perform data ETL in R.</p> <p>Data exploration examples are provided that demonstrate how powerful data visualization and machine learning is in discovering hidden relationship. You will then dive into important machine learning topics, including data classification, regression, clustering, association rule mining, and dimensionality reduction.</p> <h1>Style and Approach</h1> <p>This easy-to-follow guide is full of hands-on examples of data analysis with R. Each topic is fully explained beginning with the core concepts, followed by step-by-step, practical examples and concluding with detailed explanations of each concept used.</p>

Free Chapter

Getting Started with R

The Course Overview

Downloading and Installing R

Downloading and Installing RStudio

Installing and Loading Packages

Reading and Writing Data

Using R to Manipulate Data

Applying Basic Statistics

Visualizing Data

Getting a Dataset for Machine Learning

Data Exploration with RMS Titanic

Reading a Titanic Dataset from a CSV File

Converting Types on Character Variables

Detecting Missing Values

Imputing Missing Values

Exploring and Visualizing Datac

Predicting Passenger Survival with a Decision Tree

Validating the Power of Prediction with a Confusion Matrix

Assessing performance with the ROC curve

R and Statistics

Understanding Data Sampling in R

Operating a Probability Distribution in R

Working with Univariate Descriptive Statistics in R

Performing Correlations and Multivariate Analysis

Operating Linear Regression and Multivariate Analysis

Conducting an Exact Binomial Test

Performing Student's t-test

Performing the Kolmogorov-Smirnov Test

Understanding the Wilcoxon Rank Sum and Signed Rank Test

Working with Pearson's Chi-Squared Test

Conducting a One-Way ANOVA

Performing a Two-Way ANOVA

Understanding Regression Analysis

Fitting a Linear Regression Model with lm

Summarizing Linear Model Fits

Using Linear Regression to Predict Unknown Values

Generating a Diagnostic Plot of a Fitted Model

Fitting a Polynomial Regression Model with lm

Fitting a Robust Linear Regression Model with rlm

Studying a case of linear regression on SLID data

Applying the Gaussian Model for Generalized Linear Regression

Applying the Poisson model for Generalized Linear Regression

Applying the Binomial Model for Generalized Linear Regression

Fitting a Generalized Additive Model to Data

Visualizing a Generalized Additive Model

Diagnosing a Generalized Additive Model

Classification – Tree, Lazy, and Probabilistic

Preparing the Training and Testing Datasets

Building a Classification Model with Recursive Partitioning Trees

Visualizing a Recursive Partitioning Tree

Measuring the Prediction Performance of a Recursive Partitioning Tree

Pruning a Recursive Partitioning Tree

Building a Classification Model with a Conditional Inference Tree

Visualizing a Conditional Inference Tree

Measuring the Prediction Performance of a Conditional Inference Tree

Classifying Data with the K-Nearest Neighbor Classifier

Classifying Data with Logistic Regression

Classifying data with the Naïve Bayes Classifier

Neural Network and SVM

Classifying Data with a Support Vector Machine

Choosing the Cost of an SVM

Visualizing an SVM Fit

Predicting Labels Based on a Model Trained by an SVM

Training a Neural Network with neuralnet

Visualizing a Neural Network Trained by neuralnet

Predicting Labels based on a Model Trained by neuralnet

Training a Neural Network with nnet

Predicting labels based on a model trained by nnet

Model Evaluation

Estimating Model Performance with k-fold Cross Validation

Performing Cross Validation with the e1071 Package

Performing Cross Validation with the caret Package

Ranking the Variable Importance with the caret Package

Ranking the Variable Importance with the rminer Package

Finding Highly Correlated Features with the caret Package

Selecting Features Using the Caret Package

Measuring the Performance of the Regression Model

Measuring Prediction Performance with a Confusion Matrix

Measuring Prediction Performance Using ROCR

Comparing an ROC Curve Using the Caret Package

Measuring Performance Differences between Models with the caret Package

Ensemble Learning

Classifying Data with the Bagging Method

Performing Cross Validation with the Bagging Method

Classifying Data with the Boosting Method

Performing Cross Validation with the Boosting Method

Classifying Data with Gradient Boosting

Calculating the Margins of a Classifier

Calculating the Error Evolution of the Ensemble Method

Classifying Data with Random Forest

Estimating the Prediction Errors of Different Classifiers

Clustering

Clustering Data with Hierarchical Clustering

Cutting Trees into Clusters

Clustering Data with the k-Means Method

Drawing a Bivariate Cluster Plot

Comparing Clustering Methods

Extracting Silhouette Information from Clustering

Obtaining the Optimum Number of Clusters for k-Means

Clustering Data with the Density-Based Method

Clustering Data with the Model-Based Method

Visualizing a Dissimilarity Matrix

Validating Clusters Externally

Association Analysis and Sequence Mining

Transforming Data into Transactions

Displaying Transactions and Associations

Mining Associations with the Apriori Rule

Pruning Redundant Rules

Visualizing Association Rules

Mining Frequent Itemsets with Eclat

Creating Transactions with Temporal Information

Mining Frequent Sequential Patterns with cSPADE

Dimension Reduction

Performing Feature Selection with FSelector

Performing Dimension Reduction with PCA

Determining the Number of Principal Components Using the Scree Test

Determining the Number of Principal Components Using the Kaiser Method

Visualizing Nultivariate Data Using biplot

Performing Dimension Reduction with MDS

Reducing Dimensions with SVD

Compressing Images with SVD

Performing Nonlinear Dimension Reduction with ISOMAP

Performing Nonlinear Dimension Reduction with Local Linear Embedding

Big Data Analysis with R and Hadoop

Preparing the RHadoop Environment

Installing rmr2

Installing rhdfs

Operating HDFS with rhdfs

Implementing a Word Count Problem with RHadoop

Comparing the Performance between an R MapReduce Program and a Standard R Program

Testing and Debugging the rmr2 Program

Installing plyrmr

Manipulating Data with plyrmr

Conducting Machine Learning with RHadoop

Configuring RHadoop Clusters on Amazon EMR

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Chapter 3

R and Statistics

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Section 4

Performing Correlations and Multivariate Analysis

To analyze the relation among more than two variables, multivariate analysis is done. - Get the co-variance matrix - Obtain the correlation matrix