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  • history - Origin of the Naïve Bayes classifier? - Cross Validated
    A naive Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem with strong (naive) independence assumptions Bayes' theorem was named after the Reverend Thomas Bayes (1702–61), who studied how to compute a distribution for the probability parameter of a binomial distribution
  • How is Naive Bayes a Linear Classifier? - Cross Validated
    What I have continually read is that Naive Bayes is a linear classifier (ex: here) (such that it draws a linear decision boundary) using the log odds demonstration However, I simulated two Gaussian clouds and fitted a decision boundary and got the results as such (library e1071 in r, using naiveBayes())
  • Difference between Bayes classifier, KNN classifier and Naive Bayes . . .
    The Naive Bayes classifier approximates the Optimal Bayes classifier by looking at the empirical distribution and by assuming conditional independence of explanatory variables, given a class So the Naive Bayes classifier is not itself optimal, but it approximates the optimal solution
  • Why is the naive bayes classifier optimal for 0-1 loss?
    Naive Bayes classifier approximates the optimal classifier by looking at the empirical distribution and by assuming independence of predictors So naive Bayes classifier is not itself optimal, but it approximates the optimal solution In your question you seem to confuse those two things
  • The difference between the Bayes Classifier and The Naive Bayes . . .
    We can combine the two and add some connections between the features of the Naive Bayes and it becomes the tree augmented Naive Bayes or k-dependence Bayesian classifier References: 1 Bayesian Network Classifiers
  • Why Naive Bayes classifier is known to be a bad estimator?
    In scikit-learn documentation page for Naive Bayes, it states that: On the flip side, although naive Bayes is known as a decent classifier, it is known to be a bad estimator, so the probability outputs from predict_proba are not to be taken too seriously
  • Naïve Bayes Theorem for multiple features - Cross Validated
    TLDR: naive Bayes algorithm uses empirical probabilities (observed fractions) and then classifies by choosing the class that has greatest a posteriori probability (that's the argmax part) As simple as that $\endgroup$
  • bayesian - Difference between naive Bayes multinomial naive Bayes . . .
    The distribution you had been using with your Naive Bayes classifier is a Guassian p d f , so I guess you could call it a Guassian Naive Bayes classifier In summary, Naive Bayes classifier is a general term which refers to conditional independence of each of the features in the model, while Multinomial Naive Bayes classifier is a specific
  • Best feature selection method for naive Bayes classification
    i want to make classification with naive Bayes I have got about 100 Features Numerical ones as well as categorical ones Since i want only the most relevant ones to be included for the classification task i want to find them with some kind of feature elimination
  • Naive Bayes: Continuous and Categorical Predictors
    $\begingroup$ You can certainly do this, and it would be an ensemble classifier, but it still isn't exactly the same prediction as a single "mixed" naive Bayes model, since nb predict_proba(x1) returns normalized class marginal probabilities





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