Applied Supervised Learning with R

Applied Supervised Learning with R

By : Karthik Ramasubramanian, Jojo Moolayil

Buy this Book

Applied Supervised Learning with R

By: Karthik Ramasubramanian, Jojo Moolayil

Buy this Book

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.

Applied Supervised Learning with R

Preface

Free Chapter

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

Boxplot

Summary

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

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

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

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

Feature Selection and Dimensionality Reduction

Highly Correlated Variables

Feature Reduction

Variable Clustering

Linear Discriminant Analysis for Feature Reduction

Summary

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

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

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

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

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Linear Regression

Let's revisit the multiple linear regression from Chapter 3, Introduction to Supervised Learning. The following equation is the mathematical representation of a linear equation, or linear predictor function, with p explanatory variables and n observations:

Where each is a vector of column values (explanatory variable) and is the unknown parameters or coefficients. , makes this equation suitable for simple linear regression. There are many algorithms to fit this function onto the data. The most popular one is Ordinary Least Square (OLS).

Before understanding the details of OLS, first let's interpret the equation we got while trying to fit the Beijing PM2.5 data from the model building section of simple and multiple linear regression from Chapter 3, Introduction to Supervised Learning.