# 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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### Need suggestion/guide on how to estimate unknown bayesian priors

Suppose I can only observe people who visit Starbucks. My posterior probabilities will be like $\Pr(\text{male} \mid \text{visits Starbucks})$, $\Pr(\text{has hair} \mid \text{visits Starbucks})$, ...
1answer
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### How to use Naive Bayes with “not found” label?

I'm trying to do text detection thanks to Naive Bayes Algorithm. If I teach my tool: "Football is a great hobby" and assign it to the label "football", I'm totally fine with it detecting "I play ...
2answers
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### Confused among Gaussian, Multinomial and Binomial Naive Bayes for Text Classification

I am doing text classification but I am confused which Naive Bayes model I should use. What I understood by reading answers from couple of places that Gaussian Naive Bayes can be used if the attribute ...
0answers
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### Can inferencing come from incomplete rule sets?

I have some data for medical diagnosis, consisting of some rules about relationship of diseases and their symptoms, for example disease D1 frequently has symptom S1 ...
1answer
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### Naive Bayes logarithmic probability

I am trying to do sentiment analysis using Naive Bayes and have a doubt regarding log. While calculating posterior probability in Naive Bayes classifier, we apply log to prevent underflows and very ...
1answer
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### How does the beta prior - binomial conjugate theory hold in classifiers?

In our machine learning class, we were given an example of a naive bayes classifier. Say, you classify a day as being good/bad depending on 2 conditions (the "X" input) - weather(X1 - hot/cold) and ...
1answer
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### Small Probabilities in Naive Bayes

I am trying to implement Naive Bayes, but I am encountering a problem. I have 5000 word features. Hence, every sample is a binary vector of length 5000. The true labels are 1 or 0. The value of P(...
1answer
276 views

### What non-Bayesian classifiers could be used in a naive Bayes model?

Bayes' theorem [is used] in the classifier's decision rule, but naive Bayes is not (necessarily) a Bayesian method. -Wikipedia. I have applied naive Bayes assumption to LDA, but it has a Bayes ...
1answer
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### Training and validating a naive gaussian bayes classifier

I've been reading up on the theory of naive bayes classifiers, specifically the ones in which the probability $p(x|c)$ is a random vector. The problem which I'm trying to solve takes a 4 dimensional ...
1answer
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### Classification problem: compare results of a decision tree, naive bayes and 1-nearest-neighbor classifier

These are the results of a classification problem using decision tree, naive bayes and 1-nearest-neighbor as classifiers. There are 10,000 data objects and these results were validated using 10-fold ...
2answers
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### Bernoulli NB vs MultiNomial NB, How to choose among different NB algorithms?

I want to understand the logic behind using a specific type of NB algorithm for a particular dataset. I read about Naive Bayes but still few things are unclear. According to my understanding of NB ...
0answers
478 views

### Naive bayes performs worse than predicting the most common answer?

I have input X, with 22 binary features and 70000 examples. The target y is one of 4 possible categories. They are unbalanced, with the most common having a bit more than 51% of the data. When I train ...
2answers
3k views

### Naive Bayes: Mix unigrams and bigrams for text classification?

I'm creating a naive bayes text classifier, but I'm wondering if it's a good idea to break the text up into both unigrams and bigrams. Should I only use one method? Will having both variations mess ...
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### Analogy between Neural network and naive bayes

I am trying to understand the analogy between a single layer neural network and naive Bayes classifier. Particularly, I want to know if, in a neural network, the variables are independent given the ...