更新时间：2021-06-24 14:21:20
coverpage
Title Page
Copyright and Credits
Training Systems Using Python Statistical Modeling
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About the author
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Preface
Who this book is for
What this book covers
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Download the example code files
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Conventions used
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Classical Statistical Analysis
Technical requirements
Computing descriptive statistics
Preprocessing the data
Computing basic statistics
Classical inference for proportions
Computing confidence intervals for proportions
Hypothesis testing for proportions
Testing for common proportions
Classical inference for means
Computing confidence intervals for means
Hypothesis testing for means
Testing with two samples
One-way analysis of variance (ANOVA)
Diving into Bayesian analysis
How Bayesian analysis works
Using Bayesian analysis to solve a hit-and-run
Bayesian analysis for proportions
Conjugate priors for proportions
Credible intervals for proportions
Bayesian hypothesis testing for proportions
Comparing two proportions
Bayesian analysis for means
Credible intervals for means
Bayesian hypothesis testing for means
Finding correlations
Testing for correlation
Summary
Introduction to Supervised Learning
Principles of machine learning
Checking the variables using the iris dataset
The goal of supervised learning
Training models
Issues in training supervised learning models
Splitting data
Cross-validation
Evaluating models
Accuracy
Precision
Recall
F1 score
Classification report
Bayes factor
Binary Prediction Models
K-nearest neighbors classifier
Training a kNN classifier
Hyperparameters in kNN classifiers
Decision trees
Fitting the decision tree
Visualizing the tree
Restricting tree depth
Random forests
Optimizing hyperparameters
Naive Bayes classifier
Training the classifier
Support vector machines
Training a SVM
Logistic regression
Fitting a logit model
Extending beyond binary classifiers
Multiple outcomes for decision trees
Multiple outcomes for random forests
Multiple outcomes for Naive Bayes
One-versus-all and one-versus-one classification
Regression Analysis and How to Use It
Linear models
Fitting a linear model with OLS
Performing cross-validation
Evaluating linear models
Using AIC to pick models
Bayesian linear models
Choosing a polynomial
Performing Bayesian regression
Ridge regression
Finding the right alpha value
LASSO regression
Spline interpolation
