Training Systems Using Python Statistical Modeling电子书

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Preface

Who this book is for

What this book covers

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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

Testing with two samples

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

Summary

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

Preprocessing the data

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

Summary

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

Using SciPy for interpolation

2D interpolation

Summary

Neural Networks

An introduction to perceptrons

Neural networks

The structure of a neural network

Types of neural networks

The MLP model

MLPs for classification

Optimization techniques

Training the network

Fitting an MLP to the iris dataset

Fitting an MLP to the digits dataset

MLP for regression

Summary

Clustering Techniques

Introduction to clustering

Computing distances

Exploring the k-means algorithm

Clustering the iris dataset

Compressing images with k-means

Evaluating clusters

The elbow method

The silhouette method

Hierarchical clustering

Clustering the iris dataset

Clustering the Headlines dataset

Spectral clustering

Clustering the Headlines dataset

Summary

Dimensionality Reduction

Introducing dimensionality reduction

Uses of dimensionality reduction

Principal component analysis

Demonstration of PCA

Choosing the number of components

Singular value decomposition

SVD for image compression

Low-rank approximation

Reconstructing the image using compact SVD

Low-dimensional representation

Example of MDS

MDS in action

How MDS comes into the picture

Constructing distances

Summary

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