Naive Bayes classifier - Wikipedia

For example, the naive Bayes classifier will make the correct MAP decision rule classification so long as the correct class is more probable than any other class. This is true regardless of whether the probability estimate is slightly, or even grossly inaccurate. In this manner, the overall classifier can be robust enough to ignore serious deficiencies in its underlying naive probability model.

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Boosting (machine learning) - Wikipedia

While boosting is not algorithmically constrained, most boosting algorithms consist of iteratively learning weak classifiers with respect to a distribution and adding them to a final strong classifier. When they are added, they are weighted in a way that is related to the weak learners' accuracy. After a weak learner is added, the data weights are readjusted, known as "re-

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ML Algorithms: One SD (σ)- Bayesian Algorithms

The features/predictors used by the classifier are the frequency of the words present in the document. Some things to consider: Used with discrete data. Works well for data which can easily be turned into counts, such as word counts in text. · Averaged One-Dependence Estimators (AODE) AODE is a semi-naive Bayesian Learning method. It was developed to address the attribute independence problem

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How to Make Predictions with scikit-learn

How to predict classification or regression outcomes with scikit-learn models in Python. Once you choose and fit a final machine learning model in scikit-learn, you can use it to make predictions on new data instances. There is some confusion amongst beginners about how exactly to do this. I often see questions such as: How do I make predictions with my model in scikit-learn?

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How the Naive Bayes Classifier works in

Naive Bayes classifier is a straightforward and powerful algorithm for the classification task. Even if we are working on a data set with millions of records with some attributes, it is suggested to try Naive Bayes approach. Naive Bayes classifier gives great results when we use it for textual data analysis. Such as Natural Language Processing.

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The Best Classifiers, Sieves -

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