Applied Supervised Learning with R

封面

版权页

Preface

About

About the Book

Chapter 1: 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

Chapter 2: 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

Chapter 3: 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

Chapter 4: 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

Chapter 5: 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

Chapter 6: 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

Chapter 7: 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