machine learning bayes theorem

This article by leftnoteasy original, can be reproduced, but please keep the source and this line, if there is commercial purpose, please contact the author wheeleast@gmail.com I. Bayesian theorem: Bayesian Theorem explains common knowledge that everyone knows in life using mathematical methods The simplest theorem of form is often the best

Summary of machine learning problems Category Name Keywords Supervised Classification Decision tree Information Gain Classification regression tree Gini index, Gini 2 Statistics, pruning Naive Bayes Non-parameter estimation, Bayesian Estimation Linear Discriminant Analysis Fishre identification, fe

", assume that the position of the word "nips" is 35000 In the dictionary. However, the word "nips" has never been used in training data. This is the first time that the word "nips" appears. Therefore, when calculating the probability: Since nips never appear in spam or normal emails, the result can only be 0. The posterior probability is as follows: In this case, we can use Laplace smoothing. for unused features, we assign a small value instead of 0. The specific smoothing method is:

categorized, that is, the training set.(2) The conditional probability estimation of each characteristic attribute under various types is obtained by statistic. That(3) If each characteristic attribute is conditionally independent (or if they are independent of each other), then the Bayesian theorem is deduced as follows:Because the denominator is the same for all categories, it is constant, so just maximize the numerator. And because a feature attri

combat6.3.1 mllib linear regression basic preparation6.3.2 Mllib Linear regression combat: the relationship between commodity price and consumer income6.3.3 Verification of fitting curve6.4 SummaryThe 7th Chapter Mllib classification actual combat7.1 Logistic Regression explanation7.1.1 Logistic regression is not a regression algorithmThe mathematical basis of 7.1.2 logistic regression7.1.31-Dollar Logistic regression example7.1.4 multi-Element Logistic regression example7.1.5 Mllib Logistic re

algorithm, decision tree, Naive Bayes, logistic regression, support vector machine, etc.Unsupervised learning (unsupervised learning): Contrary to supervised learning, the data set is completely untagged, the main basis is that similar samples in the data space of the gener

To learn about machine learning, you must master a few mathematical knowledge. Otherwise, you will be confused (Allah was in this state before ). Among them, data distribution, maximum likelihood (and several methods for extreme values), deviation and variance trade-offs, as well as feature selection, model selection, and hybrid model are all particularly important. Here I will take you to review the releva

Dialogue machine learning Great God Yoshua Bengio (Next)Professor Yoshua Bengio (Personal homepage) is one of the great Gods of machine learning, especially in the field of deep learning. Together with Geoff Hinton and Professor Yann LeCun (Yan), he created the deep

the algorithm must produce a model, but often in defining what is "good" above we are prone to divergence, therefore, for a given data set, the role of inductive preference (inductive bias) is equivalent to "values", And for datasets that can produce multiple possible assumptions, inductive preference is important because it ensures that the same results are obtained every time .NFL theorem: There is no free lunch

Bayesian LearningAlgorithmThere are two reasons for applying it to machine learning: first, Bayesian learning can calculate the explicit hypothesis probability, as shown in Naive Bayes classifier. Second: Bayesian method provides a means for understanding other methods of machine