Training Systems Using Python Statistical Modeling: Explore Popular Techniques for Modeling Your Data in Python.
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| Title: | Training Systems Using Python Statistical Modeling: Explore Popular Techniques for Modeling Your Data in Python. |
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| Authors: | Miller Curtis. |
| Source: | EN05CEBSCO05C30307 |
| Publisher Information: | Birmingham Packt Publishing, Limited |
| Publication Year: | 2019 |
| Collection: | Kazan Federal University Digital Repository |
| Subject Terms: | COMPUTERS -- Programming Languages -- Python, Python (Computer program language), Graphical modeling (Statistics), Electronic books |
| Description: | Description based upon print version of record. ; The silhouette method ; This book will acquaint you with various aspects of statistical analysis in Python. You will work with different types of prediction models, such as decision trees, random forests and neural networks. By the end of this book, you will be confident in using various Python packages to train your own models for effective machine learning. ; Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: 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 worksUsing 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; Chapter 2: Introduction to Supervised Learning; Principles of machine learning; Checking the variables using the iris dataset; The goal of supervised learning ; Training modelsIssues in training supervised learning models; Splitting data; Cross-validation; Evaluating models; Accuracy; Precision; Recall; F1 score; Classification report; Bayes factor; Summary; Chapter 3: 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; ... |
| Document Type: | book |
| Language: | English |
| ISBN: | 978-1-83882-064-0 1-83882-064-7 |
| Relation: | http://dspace.kpfu.ru/xmlui/bitstream/net/178221/-1/2142584.pdf; https://dspace.kpfu.ru/xmlui/handle/net/178221 |
| Availability: | https://dspace.kpfu.ru/xmlui/handle/net/178221 |
| Accession Number: | edsbas.280182D |
| Database: | BASE |
Nájsť tento článok vo Web of Science | Abstract: | Description based upon print version of record. ; The silhouette method ; This book will acquaint you with various aspects of statistical analysis in Python. You will work with different types of prediction models, such as decision trees, random forests and neural networks. By the end of this book, you will be confident in using various Python packages to train your own models for effective machine learning. ; Cover; Title Page; Copyright and Credits; About Packt; Contributors; Table of Contents; Preface; Chapter 1: 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 worksUsing 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; Chapter 2: Introduction to Supervised Learning; Principles of machine learning; Checking the variables using the iris dataset; The goal of supervised learning ; Training modelsIssues in training supervised learning models; Splitting data; Cross-validation; Evaluating models; Accuracy; Precision; Recall; F1 score; Classification report; Bayes factor; Summary; Chapter 3: 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; ... |
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| ISBN: | 9781838820640 1838820647 |