last updated on october 25 2019. in this tutorial you are going to learn about the naive bayes algorithm including how it works and how to implement it from scratch in python (without libraries).. we can use probability to make predictions in machine learning. perhaps the most widely used example is called the naive bayes algorithm.
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to be effective a "random forest" of naive bayes classifiers or any other stable base classifier such as svms needs the addition of stochastic element. for stable classifiers relatively small variations in training data such as arise from bagging lead to very similar classifiers. to increase diversity other approaches could be applied.
learn how the naive bayes classifier algorithm works in machine learning by understanding the bayes theorem with real life examples. this course will cover the basic components of building and applying prediction functions with an emphasis on practical applications.
in machine learning support-vector machines (svms also support-vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis.given a set of training examples each marked as belonging to one or the other of two categories an svm training algorithm builds a model that assigns new examples to one category