更新时间：2021-07-09 18:24:30
封面
版权信息
Credits
About the Author
About the Reviewers
Packt Upsell
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for
Conventions
Reader feedback
Customer support
Downloading the example code
Downloading the color images of this book
Errata
Piracy
Questions
A Process for Success
The process
Business understanding
Identifying the business objective
Assessing the situation
Determining the analytical goals
Producing a project plan
Data understanding
Data preparation
Modeling
Evaluation
Deployment
Algorithm flowchart
Summary
Linear Regression - The Blocking and Tackling of Machine Learning
Univariate linear regression
Multivariate linear regression
Data understanding and preparation
Modeling and evaluation
Other linear model considerations
Qualitative features
Interaction terms
Logistic Regression and Discriminant Analysis
Classification methods and linear regression
Logistic regression
The logistic regression model
Logistic regression with cross-validation
Discriminant analysis overview
Discriminant analysis application
Multivariate Adaptive Regression Splines (MARS)
Model selection
Advanced Feature Selection in Linear Models
Regularization in a nutshell
Ridge regression
LASSO
Elastic net
Business case
Best subsets
Cross-validation with glmnet
Regularization and classification
Logistic regression example
More Classification Techniques - K-Nearest Neighbors and Support Vector Machines
K-nearest neighbors
Support vector machines
KNN modeling
SVM modeling
Feature selection for SVMs
Classification and Regression Trees
An overview of the techniques
Understanding the regression trees
Classification trees
Random forest
Gradient boosting
Regression tree
Classification tree
Random forest regression
Random forest classification
Extreme gradient boosting - classification
