Applied Supervised Learning with Python

封面

版权页

Preface

About the Book

Chapter 1 Python Machine Learning Toolkit

Introduction

Supervised Machine Learning

Jupyter Notebooks

pandas

Data Quality Considerations

Summary

Chapter 2 Exploratory Data Analysis and Visualization

Introduction

Summary Statistics and Central Values

Missing Values

Distribution of Values

Relationships within the Data

Summary

Chapter 3 Regression Analysis

Introduction

Regression and Classification Problems

Linear Regression

Multiple Linear Regression

Autoregression Models

Summary

Chapter 4 Classification

Introduction

Linear Regression as a Classifier

Logistic Regression

Classification Using K-Nearest Neighbors

Classification Using Decision Trees

Summary

Chapter 5 Ensemble Modeling

Introduction

Overfitting and Underfitting

Bagging

Boosting

Summary

Chapter 6 Model Evaluation

Introduction

Evaluation Metrics

Splitting the Dataset

Performance Improvement Tactics

Summary

Appendix

Chapter 1: Python Machine Learning Toolkit

Chapter 2: Exploratory Data Analysis and Visualization

Chapter 3: Regression Analysis

Chapter 4: Classification

Chapter 5: Ensemble Modeling