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>

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R Machine Learning solutions [Video]
Yu-Wei, Chiu (David Chiu)
Nov, 2016 | 8 hours 20 minutes
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Table of Contents (12 chapters)
Free Chapter
1
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
2
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
3
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
4
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
5
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
6
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
Tuning 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
7
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
8
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
9
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
10
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
11
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
12
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
Chapter 8
Ensemble Learning
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Section 3
Classifying Data with the Boosting Method
Boosting starts with a simple or weak classifier and gradually improves it by reweighting the misclassified samples. Thus, the new classifier can learn from previous classifiers. One can use the boosting method to perform ensemble learning. Let’s see how to use the boosting method to classify the telecom churn dataset. - Use the boosting function from the adabag package - Make a prediction based on the boosted model and testing dataset - Retrieve the classification table and obtain average errors
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