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Questions tagged [naive-bayes]

A naive Bayes classifier is a simple probabilistic classifier based on applying Bayes' theorem with strong independence assumptions. A more descriptive term for the underlying probability model would be "independent feature model".

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31 views

Why is $p(y|x)$ infeasible when discussing Naive Bayes?

This is a question in which I think I am missing some key information. When discussing Naive Bayes, I've noticed that lecturers typically say that we really want is $p(y|x)$ (label given features), ...
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Which Naive Bayes Classifier fits best for categorical data? [closed]

Which Naive Bayes Classifier would you choose to fit data with two categorical variables and a class variable? weather=['Sunny','Sunny','Overcast','Rainy','Rainy','Rainy','Overcast','Sunny','Sunny', ...
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Conditional Probability Given Multiple Priors

Reference: ilanman Oct 3, '16 at 13:27. Even though years old, this discussion is relevant to my current interests. The P(Ai|B) in the last formula above, reverses the position of the posterior and ...
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Bayesian repeated updates, likelihood functions with different nature

Let's say we have a prior probability of some diseases 'D'. Then we have some data and likelihood function of symptoms (S) P(S|D) and we update priors. Then we have age (A) likelihood function P(A|D) ...
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In-depth explanation of the multinomial Bayes classifier

I am new to machine learning and am trying to understand the different classifiers. I have searched the internet and books for a comprehensive explanation of the Multinomial Bayes classifier, but I ...
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Decision boundary for categorical Bayesian network

I know that categorical Naive Bayes (categorical predictors, binary target) has a linear classification boundary. I'm wondering what the decision boundary for an arbitrary categorical Bayesian network ...
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Updating a probability with additional knowledge. Bayes Theorem

I am quite confused with using Bayes theorem for the following problem. And I am not sure it can be applied at all. I have a football website data with user views. Each view corresponds to a specific ...
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Size of training in Naive Bayes

I just started getting involved with Machine Learning and I decided to create a spam filter for my social app, using the Naive Bayes classifier. I'm following this guide: https://hackernoon.com/how-to-...
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What is to be done when PDFs are not Gaussian/Normal in Naive Bayes Classifier

While analyzing the data for a given problem set, I came across a few distributions which are not Gaussian in nature. They are not even uniform or Gamma distributions(so that I can write a function, ...
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Jurafsky and Martin (2018) Do not understand formula in naiye bayes classifier

Currently I am reading Language and Speech Processing by , Chapter 4 Naiye Bayes and Sentiment Classification. At page $7,$ when the authors discuss worked example. Data set is as follows: Training ...
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What are the relationships among Markov Property, Stationarity, and Time Invariance

I am wondering if there is or are any relationship among those. I have understood Markov Property by reading Wikipedia, but it is still confusing to figure out if there is any relationship among those ...
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Can anyone help to explain one of the variables in a figure that illustrates how posterior probabilities shift and move around?

I am learning this post. The book gives this figure to illustrate how posterior probabilities shift and move around Here is the code ...
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Which distribution should I use for Naive Bayes algorithm(Gaussian or Rayleigh)? What to do with categorical data?

I am predicting whether credit card application of an individual would be approved or not given his/her credentials. I have the following dataset: The variable descriptions are as follows: I need ...
51 views

What is the meaning of generating data from a probabilistic model such as a naive bayes classifier?

I am studying probabilistic modeling but I am stuck with the concept of generating data from the probabilistic model. Say I have built a naive bayes classification model, what is the point of ...
55 views

Hoes does laplace smoothing in Naive Bayes control high bias and high variance?

I'm trying to understand how laplace smoothing exactly helps to balance between overfitting and underfitting. I know that Laplace smoothing is used as a fail safe probability if there's a any ...
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Bayesian Formula for multiple events

I know that Bayesian Formula for A giving B is like this $p(A | B) = \frac{p(B|A) p(A)}{p(B)}$ In case there are multiple events B C D What will the equation be like in the simplest form of a ...
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To say my model is a stochastic model,what assumptions do I need to make?

I am trying to understand what a stochastic model is and assumptions to be able to say my model is a stochastic model. I am new to it, so I may confuse you. I have gone through Markov chain, Markov ...
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How to determine the discount percentage of a product for a given product category and brand?

We are performing the analysis of data of an online shopping site. Please refer to the dataset mentioned in this link The fields of the dataset are: We have been asked to do the following: Perform ...
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Is Chow Liu's scoring algorithm to have at most one root node?

I am told that Chow Liu's algorithm can have at most one root node. In the fisr place what does it mean? I am wondering how I can apply Chow Liu's scoring function for more than one root node to do a ...
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What is the difference between Bayesian Network and Dynamic Bayesian Network?

I just got the sentences below from a web site while studying Bayesian Network: "​A dynamic Bayesian network (DBN) is a Bayesian network extended with additional mechanisms that are capable of ...
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Naive Bayes linearity [duplicate]

By theory I know that Naive Bayes Classifier is a linear one, but when I implemented the decision boundary it was a curve (not linear as shown below). Is there any explanation why this is happening? ...
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How valid is this Stacking Model (input features to weak learners are different)?

I have a set of features with 6 of them being categorical, 1 continuous and 2 textual in type. I have to predict the labels ( 10 in number) for them. I tried applying several models and came to a ...
112 views

Counting frequencies in Multinomial Naive Bayes

I have noticed some ambiguity/inconsistency in how various authors calculate the p(word|label) estimate in Multinomial Naive Bayes. In some cases, this value is calculated by counting words without ...
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Derivation of the formula for the probability of a class, given conditionally independent attributes

The following is a formula that finds the posterior probability of a class (i.e. yes or no) given four conditionally independent attributes: P(c|X) = P(x_1|c)\cdot P(x_2|c)\cdot P(x_3|c)\cdot P(x_4|...
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Calculating priors with large number of classes

I'm trying to classify users into ~1K different groups. I'm trying to build a MAP classifier and have estimated my prior and posterior distributions using large amounts of data. The issue that I've ...
130 views

Gaussian Naive Bayes Classifier

There are three nice Bayes classifier techniques: Bernoulli, Multinomial, and Gaussian If we have a dataset whose samples have continuous-valued features, then Gaussian Bayes Classifier is used. In ...
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Determining the decision boundary for Naive Bayes

I'd like to know if this is a sensible idea and if there exist any already formed methods to do this (I'm new to the data science area). Essentially, I have used Naive Bayes to accurately classify ...
33 views

manual implementation of Gaussian naive bayesian returns posterior larger than 1

I try to implement Gaussian naive bayesian manually in R. I test my model on iris data set. I would like to build a predictive model. That is, I would like to ...
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Naive bayes computation of denominator

I'm wondering about the denominator in this computation : ...
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Naive bayes example by hand

Given the following data ...
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Naive Bayes missclassification rate across classes

I have a dataset with income, age sex and education as categorical features. I used R to create a Naive Bayes classifier as follows: income ~ age + sex + education. I got the following a-priori and ...
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Why is it easier to incorporate arbitrary features into discriminative models?

It is often stated, that when arbitrary features are implied, generative models (e.g. Naive Bayes) are a lesser fit than discriminative ones, mainly for being harder to build. How would you elucidate ...
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Classification with ONLY categorical data

Suppose I have a table with some factor characteristics of some plants. For instance, petal color, pollen color, and so on. What is the best way to classify that data? Is it feasible to use some of ...
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Are there useful applications for Bayes Nets (vs. Naive Bayes)?

I am trying to learn about Bayesian networks and try to make them work in the context of a simple prediction problem. But my question is more theoretical: For argument's sake, assume we have a ...
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How to make a prediction with Bayes Classifier after computing MLE?

I'm trying to figure out the role of computing the MLE for classification/prediction purposes with the Bayes Classifier. Let's say I'm given a set of data assumed to be Gaussian. I can then compute ...
Suppose that the unknown parameter $\Theta$ is Bernoulli and we make $n$ observations $X_1,X_2,\ldots,X_n$, which are continuous random variables. Assuming that $X_1,X_2,\ldots,X_n$ are conditionally ...