Binary And Multiclass Classification
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Binary and Multiclass Classification
Classifiers are 'black boxes' that examine an input and place the input into a category. Examples include medical diagnostics (presence/absence of a disease), quality control (is a part defective?), search engine results (does a page match the search criteria?), language identification (what is the language of a document?), and data quality metrics (does this data set have sufficient quality metrics?). Increasingly, software tools are developed that implement or automate the classification process. Competing classification algorithms are compared to determine which is better suited for a particular application. This book develops the tools needed to measure classifier performance, compare classifiers, and rank order the results. This text examines the properties of binary and multiclass classifiers from a general perspective. Topics include classifier performance metrics, error analysis, comparison of classifier performance, metric distributions, and receiver operator characteristics (ROC).
Introductory Statistics with R
Author: Peter Dalgaard
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-06-27
This book provides an elementary-level introduction to R, targeting both non-statistician scientists in various fields and students of statistics. The main mode of presentation is via code examples with liberal commenting of the code and the output, from the computational as well as the statistical viewpoint. Brief sections introduce the statistical methods before they are used. A supplementary R package can be downloaded and contains the data sets. All examples are directly runnable and all graphics in the text are generated from the examples. The statistical methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way analysis of variance, regression analysis, analysis of tabular data, and sample size calculations. In addition, the last four chapters contain introductions to multiple linear regression analysis, linear models in general, logistic regression, and survival analysis.
Ensemble Learning Algorithms With Python
Author: Jason Brownlee
language: en
Publisher: Machine Learning Mastery
Release Date: 2021-04-26
Predictive performance is the most important concern on many classification and regression problems. Ensemble learning algorithms combine the predictions from multiple models and are designed to perform better than any contributing ensemble member. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will discover how to confidently and effectively improve predictive modeling performance using ensemble algorithms.