Practical Machine Learning in R
Practical Machine Learning in R
About the Book
The book is about quickly entering the world of creating machine learning models in R. The theory is kept to minimum and there are examples for each of the major algorithms for classification, clustering, features engineering and association rules.
The book is a compilation of the leaflets the authors give to their students during the practice labs, in the courses of Pattern Recognition and Data Mining, in the Electrical and Computer Engineering Department of the Aristotle University of Thessaloniki.
About the Contributors
Table of Contents
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Part I - Introduction
-
Chapter 1 - Introduction to R
- 1.1 The basics
- 1.2 Vectors
- 1.3 Matrices
- 1.4 Data frames
- 1.5 R Scripts
- 1.6 Functions
-
1.7
forloops -
1.8 Making decisions (
if&else) - 1.9 Datasets and statistics
- 1.10 Factors
- 1.11 Challenge
-
Chapter 2 - Introduction to Machine Learning
- 2.1 Definition
- 2.2 Main categories and tasks
- 2.3 Survival guide to machine learning
-
Chapter 1 - Introduction to R
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Part II - Classification
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Chapter 3 - Classification with Decision trees
- 3.1 Introduction
- 3.2 Splitting Criteria and Decision Tree Construction
- 3.3 Application with Pruning and Evaluation Metrics
- 3.4 Exercise
-
Chapter 4 - Classification with Naive Bayes
- 4.1 Introduction
- 4.2 Naive Bayes Model Construction and Classification
- 4.3 Naive Bayes Application with Evaluation
-
Chapter 5 - Classification with k-Nearest Neighbors
- 5.1 Introduction
- 5.2 k-Nearest Neighbors Model Construction and Classification
- 5.3 k-Nearest Neighbors Real World Example
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Chapter 6 - Classification with Support Vector Machines
- 6.1 Introduction
- 6.2 Support Vector Machines Model Construction and Classification
- 6.3 Exercise
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Chapter 3 - Classification with Decision trees
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Part III - Data Processing
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Chapter 7 - Feature Selection
- 7.1 Introduction
- 7.2 Filter Methods
- 7.2 Wrapper Methods
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Chapter 8 - Dimensionality Reduction
- 8.1 Principal Components Analysis
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Chapter 7 - Feature Selection
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Part III - Clustering
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Chapter 9 - Centroid-based Clustering and Evaluation
- 9.1 k-Means in R
- 9.2 k-Medoids in R
- 9.3 Clustering Evaluation in R
- 9.4 k-Means clustering overview
- 9.5 k-Means clustering and evaluation in real life Application
- 9.6 k-Medoids Application
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Chapter 10 - Connectivity-based Clustering
- 10.1 Hierarchical clustering in R
- 10.2 Hierarchical Clustering Application and Evaluation
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Chapter 11 Density-based Clustering
- 11.1 - DBSCAN in R
- 11.2 DBSCAN model construction
- 11.3 DBSCAN calculation
- 11.4 DBSCAN clustering with R
- 11.5 Density-based Clustering Application
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Chapter 12 - Distribution-based Clustering
- 12.1 Theoretical background
- 12.2 Modeling Gaussian Mixture Models using EM
- 12.3 GMMs Clustering Application
- 12.4 GMMs Application with Information Criteria
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Chapter 9 - Centroid-based Clustering and Evaluation
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Part V - Extended Topics
- Chapter 13 - Association Rules
- Notes
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