Naive Bayes Classifier
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Naive Bayes Classifier
Author: Fouad Sabry
language: en
Publisher: One Billion Knowledgeable
Release Date: 2023-06-23
What Is Naive Bayes Classifier In the field of statistics, naive Bayes classifiers are a family of straightforward "probabilistic classifiers" that are derived from the application of Bayes' theorem with strong (naive) assumptions of independence between the features. They are among the Bayesian network models that are the simplest, but when combined with kernel density estimation, they are capable of achieving great levels of accuracy. How You Will Benefit (I) Insights, and validations about the following topics: Chapter 1: Naive Bayes classifier Chapter 2: Likelihood function Chapter 3: Bayes' theorem Chapter 4: Bayesian inference Chapter 5: Multivariate normal distribution Chapter 6: Maximum likelihood estimation Chapter 7: Bayesian network Chapter 8: Naive Bayes spam filtering Chapter 9: Marginal likelihood Chapter 10: Dirichlet distribution (II) Answering the public top questions about naive bayes classifier. (III) Real world examples for the usage of naive bayes classifier in many fields. (IV) 17 appendices to explain, briefly, 266 emerging technologies in each industry to have 360-degree full understanding of naive bayes classifier' technologies. Who This Book Is For Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of naive bayes classifier.
Thoughtful Machine Learning
Author: Matthew Kirk
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2014-09-26
Learn how to apply test-driven development (TDD) to machine-learning algorithms—and catch mistakes that could sink your analysis. In this practical guide, author Matthew Kirk takes you through the principles of TDD and machine learning, and shows you how to apply TDD to several machine-learning algorithms, including Naive Bayesian classifiers and Neural Networks. Machine-learning algorithms often have tests baked in, but they can’t account for human errors in coding. Rather than blindly rely on machine-learning results as many researchers have, you can mitigate the risk of errors with TDD and write clean, stable machine-learning code. If you’re familiar with Ruby 2.1, you’re ready to start. Apply TDD to write and run tests before you start coding Learn the best uses and tradeoffs of eight machine learning algorithms Use real-world examples to test each algorithm through engaging, hands-on exercises Understand the similarities between TDD and the scientific method for validating solutions Be aware of the risks of machine learning, such as underfitting and overfitting data Explore techniques for improving your machine-learning models or data extraction
Machine Learning with Python Cookbook
Author: Chris Albon
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2018-03-09
This practical guide provides nearly 200 self-contained recipes to help you solve machine learning challenges you may encounter in your daily work. If you’re comfortable with Python and its libraries, including pandas and scikit-learn, you’ll be able to address specific problems such as loading data, handling text or numerical data, model selection, and dimensionality reduction and many other topics. Each recipe includes code that you can copy and paste into a toy dataset to ensure that it actually works. From there, you can insert, combine, or adapt the code to help construct your application. Recipes also include a discussion that explains the solution and provides meaningful context. This cookbook takes you beyond theory and concepts by providing the nuts and bolts you need to construct working machine learning applications. You’ll find recipes for: Vectors, matrices, and arrays Handling numerical and categorical data, text, images, and dates and times Dimensionality reduction using feature extraction or feature selection Model evaluation and selection Linear and logical regression, trees and forests, and k-nearest neighbors Support vector machines (SVM), naïve Bayes, clustering, and neural networks Saving and loading trained models