Book Image

Applied Supervised Learning with R

By : Karthik Ramasubramanian, Jojo Moolayil
Book Image

Applied Supervised Learning with R

By: Karthik Ramasubramanian, Jojo Moolayil

Overview of this book

R provides excellent visualization features that are essential for exploring data before using it in automated learning. Applied Supervised Learning with R helps you cover the complete process of employing R to develop applications using supervised machine learning algorithms for your business needs. The book starts by helping you develop your analytical thinking to create a problem statement using business inputs and domain research. You will then learn different evaluation metrics that compare various algorithms, and later progress to using these metrics to select the best algorithm for your problem. After finalizing the algorithm you want to use, you will study the hyperparameter optimization technique to fine-tune your set of optimal parameters. The book demonstrates how you can add different regularization terms to avoid overfitting your model. By the end of this book, you will have gained the advanced skills you need for modeling a supervised machine learning algorithm that precisely fulfills your business needs.

Related Content you might be interested in

Current Title:

Small book image
Applied Supervised Learning with R
Karthik Ramasubramanian | Jojo Moolayil
May, 2019 | 502 pages
Small book image
Practical Machine Learning with R
Brindha Priyadarshini Jeyaraman | Monicah Wambugu | Ludvig Renbo Olsen
Aug, 2019 | 416 pages
Small book image
Mastering Machine Learning with R
Cory Lesmeister
Apr, 2017 | 420 pages
Small book image
Regression Analysis with R
Giuseppe Ciaburro
Jan, 2018 | 422 pages
Table of Contents (12 chapters)
Applied Supervised Learning with R
Applied Supervised Learning with R
Preface
Preface
Free Chapter
1
R for Advanced Analytics
R for Advanced Analytics
Introduction
Working with Real-World Datasets
Reading Data from Various Data Formats
Write R Markdown Files for Code Reproducibility
Data Structures in R
DataFrame
Data Processing and Transformation
The Apply Family of Functions
Useful Packages
Data Visualization
Line Charts
Histogram
Boxplot
Summary
2
Exploratory Analysis of Data
Exploratory Analysis of Data
Introduction
Defining the Problem Statement
Understanding the Science Behind EDA
Exploratory Data Analysis
Univariate Analysis
Exploring Categorical Features
Bivariate Analysis
Studying the Relationship between Two Numeric Variables
Studying the Relationship between a Categorical and a Numeric Variable
Studying the Relationship Between Two Categorical Variables
Multivariate Analysis
Validating Insights Using Statistical Tests
Categorical Dependent and Numeric/Continuous Independent Variables
Categorical Dependent and Categorical Independent Variables
Summary
3
Introduction to Supervised Learning
Introduction to Supervised Learning
Introduction
Summary of the Beijing PM2.5 Dataset
Regression and Classification Problems
Machine Learning Workflow
Regression
Exploratory Data Analysis (EDA)
Classification
Evaluation Metrics
Summary
4
Regression
Regression
Introduction
Linear Regression
Model Diagnostics
Residual versus Fitted Plot
Normal Q-Q Plot
Scale-Location Plot
Residual versus Leverage
Improving the Model
Quantile Regression
Polynomial Regression
Ridge Regression
LASSO Regression
Elastic Net Regression
Poisson Regression
Cox Proportional-Hazards Regression Model
NCCTG Lung Cancer Data
Summary
5
Classification
Classification
Introduction
Getting Started with the Use Case
Classification Techniques for Supervised Learning
Logistic Regression
How Does Logistic Regression Work?
Evaluating Classification Models
What Metric Should You Choose?
Evaluating Logistic Regression
Decision Trees
XGBoost
Deep Neural Networks
Choosing the Right Model for Your Use Case
Summary
6
Feature Selection and Dimensionality Reduction
Feature Selection and Dimensionality Reduction
Introduction
Feature Engineering
One-Hot Encoding
Log Transformation
Feature Selection
Highly Correlated Variables
Feature Reduction
Variable Clustering
Linear Discriminant Analysis for Feature Reduction
Summary
7
Model Improvements
Model Improvements
Introduction
Bias-Variance Trade-off
Underfitting and Overfitting
Defining a Sample Use Case
Cross-Validation
Holdout Approach/Validation
K-Fold Cross-Validation
Hold-One-Out Validation
Hyperparameter Optimization
Grid Search Optimization
Random Search Optimization
Bayesian Optimization
Summary
8
Model Deployment
Model Deployment
Introduction
What is an API?
Introduction to plumber
A Brief History of the Pre-Docker Era
Docker
Amazon Web Services
Introducing AWS SageMaker
What is Amazon Lambda?
What is Amazon API Gateway?
Building Serverless ML Applications
Deleting All Cloud Resources to Stop Billing
Summary
9
Capstone Project - Based on Research Papers
Capstone Project - Based on Research Papers
Introduction
Exploring Research Work
The mlr Package
Problem Design from the Research Paper
Features in Scene Dataset
Implementing Multilabel Classifier Using the mlr and OpenML Packages
Constructing a Learner
Predictions
Summary
Appendix
Appendix
Chapter 1: R for Advanced Analytics
Chapter 2: Exploratory Analysis of Data
Chapter 3: Introduction to Supervised Learning
Chapter 4: Regression
Chapter 5: Classification
Chapter 6: Feature Selection and Dimensionality Reduction
Chapter 7: Model Improvements
Chapter 8: Model Deployment
Chapter 9: Capstone Project - Based on Research Papers

Feature Reduction

Feature reduction helps get rid of redundant variables that reduce the model efficiency in the following ways:

  • Time to develop/train the model increases.

  • Interpretation of the results becomes tedious.

  • It inflates the variance of the estimates.

In this section, we will see three feature reduction techniques that help in improving the model efficiency.

Principal Component Analysis (PCA)

N. A. Campbell and William R. Atchley in their classic paper, The Geometry of Canonical Variate Analysis, Systematic Biology, Volume 30, Issue 3, September 1981, Pages 268–280, geometrically defined a principal component analysis as a rotation of the axes of the original variable coordinate system to new orthogonal axes, called principal axes, such that the new axes coincide with directions of maximum variation of the original observation. This forms the crux of what PCA does. In other words, it represents the original variable with principal components that explain the maximum variation of the...

Previous Section
End of Section 8
Next Section