Bayes decision theory is the ideal decision procedure { but in practice it can be di cult to apply because of the limitations described in the next subsection.

Na¨ıve Bayes Classifier 3 1.

The k-nearest neighbor classifier fundamentally relies on a distance metric. 2.

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These exemplify two ways of doing classification.

The better that metric reflects label similarity, the better the classified will be. , feature values are independent given the label! This is a very bold assumption. , feature values are independent given the label! This is a very bold assumption.

8 Example in Python: Spam or Ham? 4 k-Nearest Neighbors.

I. Consider a visual metaphor: imagine we’re trying to distinguish dog images from cat images. if ~y(= y 1;y 2;:::;y i;:::;y M) where each y i is binary-, discrete-, or continuous-.

This is possible because we will be computing P(djc)P(c) P(d) for each possible class. .

g.

Naive Bayes methods are a set of supervised learning algorithms based on applying Bayes’ theorem with the “naive” assumption of conditional independence between every pair of features given the value of the class variable.

. What is the general performance of naive Bayes in ranking? In this paper, we study it by both empirical experiments and theoretical analysis.

e. Training the Naive Bayes classisifer corresponds to estimating $\vec{\theta}_{jc}$ for all $j$ and $c$ and storing them in the respective conditional probability tables (CPT).

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We review some of the variations of naive Bayes models used for text retrieval and classification, focusing on the distributional assumptions made about word occurrences in documents.

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. class=" fc-falcon">3. Given an ob-servation, they return the class most likely to have generated the observation.

The most common choice is the Minkowski distance. . You are given the following (noisy) examples: Recall that Baye’s rule allows you to rewrite the conditional probability of the class given. , feature values are independent given the label! This is a very bold assumption. Clearly this is not true.

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. Naive Bayes is a term that is collectively used for classification algorithms that are based on Bayes Theorem.

You choose to learna Naïve Bayes classifier.

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The Bayes Classifier • Use Bayes Rule! • Why did this help? Well, we think that we might be able to specify how features are “generated” by the class label Normalization Constant Likelihood Prior.

human yes no no yes mammals python no no no no non-mammals.

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