Training Systems Using Python Statistical Modeling: Explore Popular Techniques for Modeling Your Data in Python.

Miller  Curtis.

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dc.contributor.author	Miller Curtis.
dc.date.accessioned	2024-01-26T21:31:50Z
dc.date.available	2024-01-26T21:31:50Z
dc.date.issued	2019
dc.identifier.citation	Miller. Training Systems Using Python Statistical Modeling: Explore Popular Techniques for Modeling Your Data in Python. - Birmingham: Packt Publishing, Limited, 2019 - 1 online resource (284 p.) - URL: https://libweb.kpfu.ru/ebsco/pdf/2142584.pdf
dc.identifier.isbn	1838820647
dc.identifier.isbn	9781838820640
dc.identifier.uri	https://dspace.kpfu.ru/xmlui/handle/net/178221
dc.description	Description based upon print version of record.
dc.description	The silhouette method
dc.description.abstract	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.
dc.description.tableofcontents	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
dc.description.tableofcontents	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
dc.description.tableofcontents	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; Optimizing hyperparameters; Naive Bayes classifier; Preprocessing the data; Training the classifier; Support vector machines; Training a SVM; Logistic regression
dc.description.tableofcontents	Fitting a logit modelExtending 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; Chapter 4: 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
dc.description.tableofcontents	Using SciPy for interpolation2D interpolation; Summary; Chapter 5: 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; Chapter 6: 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
dc.language	English
dc.language.iso	en
dc.publisher	Birmingham Packt Publishing, Limited
dc.subject.other	COMPUTERS -- Programming Languages -- Python.
dc.subject.other	Python (Computer program language)
dc.subject.other	Graphical modeling (Statistics)
dc.subject.other	Electronic books.
dc.title	Training Systems Using Python Statistical Modeling: Explore Popular Techniques for Modeling Your Data in Python.
dc.type	Book
dc.description.pages	1 online resource (284 p.)
dc.collection	Электронно-библиотечные системы
dc.source.id	EN05CEBSCO05C30307