Difference between revisions of "Bayes"
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* [[Bayesian Deep Learning (BDL)]] | * [[Bayesian Deep Learning (BDL)]] | ||
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* [http://bookdown.org/max/FES/naive-bayes.html Feature Engineering and Selection: A Practical Approach for Predictive Models - 12.1 Naive Bayes Models | Max Kuhn and Kjell Johnson] | * [http://bookdown.org/max/FES/naive-bayes.html Feature Engineering and Selection: A Practical Approach for Predictive Models - 12.1 Naive Bayes Models | Max Kuhn and Kjell Johnson] | ||
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| + | = Naive Bayes = | ||
| + | * [http://machinelearningmastery.com/classification-as-conditional-probability-and-the-naive-bayes-algorithm/ How to Develop a Naive Bayes Classifier from Scratch in Python | Jason Brownlee - Machine Learning Mastery] | ||
| + | * [http://pathmind.com/wiki/bayes-theorem-naive-bayes A Beginner's Guide to Bayes' Theorem, Naive Bayes Classifiers and Bayesian Networks | Chris Nicholson - A.I. Wiki pathmind] | ||
A Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’. | A Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’. | ||
Revision as of 12:36, 11 October 2020
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This algorithm is based on the “Bayes’ Theorem” in probability. Due to that Naive Bayes can be applied only if the features are independent of each other since it is a requirement in Bayes’ Theorem. If we try to predict a flower type by its petal length and width, we can use Naive Bayes approach since both those features are independent. 10 Machine Learning Algorithms You need to Know | Sidath Asir @ Medium
Naive Bayes
- How to Develop a Naive Bayes Classifier from Scratch in Python | Jason Brownlee - Machine Learning Mastery
- A Beginner's Guide to Bayes' Theorem, Naive Bayes Classifiers and Bayesian Networks | Chris Nicholson - A.I. Wiki pathmind
A Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature. For example, a fruit may be considered to be an apple if it is red, round, and about 3 inches in diameter. Even if these features depend on each other or upon the existence of the other features, all of these properties independently contribute to the probability that this fruit is an apple and that is why it is known as ‘Naive’.
Two-Class Bayes Point Machine
This algorithm efficiently approximates the theoretically optimal Bayesian average of linear classifiers (in terms of generalization performance) by choosing one "average" classifier, the Bayes Point. Because the Bayes Point Machine is a Bayesian classification model, it is not prone to overfitting to the training data. - Microsoft
Bayesian Parameter Estimation
