更新时间：2021-07-02 19:06:32
coverpage
Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Why subscribe?
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
Journey from Statistics to Machine Learning
Statistical terminology for model building and validation
Machine learning
Major differences between statistical modeling and machine learning
Steps in machine learning model development and deployment
Statistical fundamentals and terminology for model building and validation
Bias versus variance trade-off
Train and test data
Machine learning terminology for model building and validation
Linear regression versus gradient descent
Machine learning losses
When to stop tuning machine learning models
Train validation and test data
Cross-validation
Grid search
Machine learning model overview
Summary
Parallelism of Statistics and Machine Learning
Comparison between regression and machine learning models
Compensating factors in machine learning models
Assumptions of linear regression
Steps applied in linear regression modeling
Example of simple linear regression from first principles
Example of simple linear regression using the wine quality data
Example of multilinear regression - step-by-step methodology of model building
Backward and forward selection
Machine learning models - ridge and lasso regression
Example of ridge regression machine learning
Example of lasso regression machine learning model
Regularization parameters in linear regression and ridge/lasso regression
Logistic Regression Versus Random Forest
Maximum likelihood estimation
Logistic regression – introduction and advantages
Terminology involved in logistic regression
Applying steps in logistic regression modeling
Example of logistic regression using German credit data
Random forest
Example of random forest using German credit data
Grid search on random forest
Variable importance plot
Comparison of logistic regression with random forest
Tree-Based Machine Learning Models
Introducing decision tree classifiers
Terminology used in decision trees
Decision tree working methodology from first principles
Comparison between logistic regression and decision trees
Comparison of error components across various styles of models
Remedial actions to push the model towards the ideal region
HR attrition data example
Decision tree classifier
Tuning class weights in decision tree classifier
Bagging classifier
Random forest classifier
Random forest classifier - grid search
AdaBoost classifier
Gradient boosting classifier
Comparison between AdaBoosting versus gradient boosting
Extreme gradient boosting - XGBoost classifier
Ensemble of ensembles - model stacking
Ensemble of ensembles with different types of classifiers
Ensemble of ensembles with bootstrap samples using a single type of classifier
K-Nearest Neighbors and Naive Bayes
K-nearest neighbors
KNN voter example
Curse of dimensionality
Curse of dimensionality with 1D 2D and 3D example
KNN classifier with breast cancer Wisconsin data example
Tuning of k-value in KNN classifier
Naive Bayes
Probability fundamentals
Joint probability
Understanding Bayes theorem with conditional probability
Naive Bayes classification
Laplace estimator
Naive Bayes SMS spam classification example
Support Vector Machines and Neural Networks
