bayesian analysis

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Consider a text classification where texts are represented as a bag-of words representation. For

instance, a text x =“BDA exam is very very easy” is represented as x = {BDA, exam, is, very, very,

easy}. Each word in a document is sampled independently from an identical distribution. A popular

machine learning approach to text classification is Naive Bayes model which tries to predict the probability

of text (x) belonging to a class (y), p(y|x), using the Bayes theorem. It assumes the class conditional

distribution p(x|y) to be independently and identically distributed across the features (x_i), the

words in the text. Assume the vocabulary size to be V.

(b) Consider the spam classification problem where the email text belong to either of two classes

“ham” or “spam”. Here is the training data

ham d1: “good” ham d2: “very good”

spam d3: “bad” spam d4: “very bad” spam d5: “very bad very bad.”

Now consider the test data d6: “good? bad very bad”. Assuming the vocabulary to be V={very, good,

bad} (treat “good?” same as “good”), to which class does the test data d6 belong to under maximum

likelihood estimation of class conditional distribution and class distribution parameters?

Algorytmy Machine Learning (ML) Matematyka Analiza Statystyczna Statystyka

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raheelakhurshid

A professional statistical analyst seeking opportunity to provide highest quality services in the following areas of Statistics and Econometric. Looking for outstanding opportunities to apply my academic credentials co Więcej

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ach5a60adf39e644

Hi, I'm working in machine learning field for last1 year and I really enjoy exploring the field. I'd like to take on this project.

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Vandana127

I have recently completed the machine learning course by Andrew Ng on coursera. I have a very good understanding of Python and have studied the scikit python package in detail which I feel should come handy to this req Więcej

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ishanhellsraiser

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sussahin

Likelihood function is just product of three binominal distributions, which can be written analytically.

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