Classification algorithms | Statistical classification | Bayesian statistics
In statistics, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features (see Bayes classifier). They are among the simplest Bayesian network models, but coupled with kernel density estimation, they can achieve high accuracy levels. Naive Bayes classifiers are highly scalable, requiring a number of parameters linear in the number of variables (features/predictors) in a learning problem. Maximum-likelihood training can be done by evaluating a closed-form expression, which takes linear time, rather than by expensive iterative approximation as used for many other types of classifiers. In the statistics literature, naive Bayes models are known under a variety of names, including simple Bayes and independence Bayes. All these names reference the use of Bayes' theorem in the classifier's decision rule, but naive Bayes is not (necessarily) a Bayesian method. (Wikipedia).
From playlist Naive Bayes Classifier
[http://bit.ly/N-Bayes] Components of the Naive Bayes classifier: the prior, the class model and the normalizer.
From playlist Naive Bayes Classifier
(ML 8.1) Naive Bayes classification
An introduction to "naive Bayes" classifiers, in which we model the features as conditionally independent given the class.
From playlist Machine Learning
(ML 8.3) Bayesian Naive Bayes (part 1)
When all the features are categorical, a naïve Bayes classifier can be made fully Bayesian by putting Dirichlet priors on the parameters and (exactly) integrating them out.
From playlist Machine Learning
(ML 8.4) Bayesian Naive Bayes (part 2)
When all the features are categorical, a naïve Bayes classifier can be made fully Bayesian by putting Dirichlet priors on the parameters and (exactly) integrating them out.
From playlist Machine Learning
(ML 8.6) Bayesian Naive Bayes (part 4)
When all the features are categorical, a naïve Bayes classifier can be made fully Bayesian by putting Dirichlet priors on the parameters and (exactly) integrating them out.
From playlist Machine Learning
Naive Bayes 3: Gaussian example
[http://bit.ly/N-Bayes] How can we use Naive Bayes classifier with continuous (real-valued) attributes? We estimate the priors and the means / variances for the Gaussians (two in this example).
From playlist Naive Bayes Classifier
A discussion of naive Bayes, a statistical classification framework.
From playlist Machine Learning
Naive Bayes Classifier | Naive Bayes Algorithm | Naive Bayes Classifier With Example | Simplilearn
🔥 Advanced Certificate Program In Data Science: https://www.simplilearn.com/pgp-data-science-certification-bootcamp-program?utm_campaign=MachineLearning-l3dZ6ZNFjo0&utm_medium=Descriptionff&utm_source=youtube 🔥 Data Science Bootcamp (US Only): https://www.simplilearn.com/data-science-bootc
From playlist Machine Learning with Python | Complete Machine Learning Tutorial | Simplilearn [2022 Updated]
Digging into Data: Supervised Classification with Logistic Regression and Naive Bayes
Our first lecture on classification, where we cover two linear methods.
From playlist Digging into Data
Naive Bayes Classifier Tutorial | Naive Bayes Classifier in R | Naive Bayes Classifier Example
( Data Science Training - https://www.edureka.co/data-science ) Watch sample class recording: http://www.edureka.co/data-science?utm_source=youtube&utm_medium=referral&utm_campaign=naive-bayes-classifier-15 Data science is the study of the generalizable extraction of knowledge from data,
From playlist Data Science Training Videos
Naive Bayes, Clearly Explained!!!
When most people want to learn about Naive Bayes, they want to learn about the Multinomial Naive Bayes Classifier - which sounds really fancy, but is actually quite simple. This video walks you through it one step at a time and by the end, you'll no longer be naive about Naive Bayes!!! Ge
From playlist StatQuest
Lecture 6 - Support Vector Machines | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3Gchxyg Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.sta
From playlist Stanford CS229: Machine Learning Full Course taught by Andrew Ng | Autumn 2018
Bayes Classifiers (2): Naive Bayes
Complexity and overfitting in Bayes classifiers; naive Bayes models
From playlist cs273a
Naive Bayes Classifier | Data Science | Edureka
( Data Science Training - https://www.edureka.co/data-science ) Watch the sample class recording: http://www.edureka.co/data-science?utm_source=youtube&utm_medium=referral&utm_campaign=naive-bayes-classifier In machine learning, Naive Bayes classifiers are a family of simple probabilist
From playlist Data Science Training Videos
Crash Course on Naive Bayes Classification
Naive Bayes is a technique from machine learning used for making classifications. Naive Bayes has all sorts of applications ranging from facial recognition to weather prediction to medical diagnoses to news classifications among others. In this webinar Kevin Dayaratna will introduce you
From playlist Short Crash Courses for Data Science & Data Engineering
Python - Classifying Text Part 1
Lecturer: Dr. Erin M. Buchanan Summer 2019 https://www.patreon.com/statisticsofdoom This one was posted way before the others - part two is here: https://youtu.be/f7HFeeUzkJQ In this video, you will learn some basic terminology for classification - how to extract features, train, and t
From playlist Natural Language Processing