Questions tagged [naive-bayes]

For questions related to the naive Bayes, which is a machine learning (or statistics) technique that is based on the Bayes' theorem.

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$\frac{P(x_1 \mid y, s = 1) \dots P(x_n \mid y, s = 1) P(y \mid s = 1)}{P(x \mid s = 1)}$ indicates that naive Bayes learners are global learners?

I am currently studying the paper Learning and Evaluating Classifiers under Sample Selection Bias by Bianca Zadrozny. In section 3. Learning under sample selection bias, the author says the following: ...
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Why do Bayesian algorithms work well with small datasets?

I read very often that Bayesian algorithms work well on small datasets. Why is that? I think it is because they might generalize more, but why is that? See also Investigating the use of Bayesian ...
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Why don't ensembling, bagging and boosting help to improve accuracy of Naive bayes classifier?

You might think to apply some classifier combination techniques like ensembling, bagging and boosting but these methods would not help. Actually, “ensembling, boosting, bagging” won’t help since their ...
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215 views

What to do 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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110 views

Why do I get small probabilities when implementing a multinomial naive Bayes text classification model?

When applying multinomial Naive Bayes text classification, I get very small probabilities (around $10e^{-48}$), so there's no way for me to know which classes are valid predictions and which ones are ...
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How can I apply naive Bayes classifier for three classes (Positive, Negative and Neutral) in text data?

I found a naive Bayes classifier for positive sentiment or a negative sentiment Citius: A Naive-Bayes Strategy for Sentiment Analysis on English Tweets. But with most available datasets online, ...
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How would the probability of a document $P(d)$ be computed in the Naive Bayes classifier?

In naive Bayes classification, we estimate the class of a document as follows $$\hat{c} = \arg \max_{c \in C} P(c \mid d) = \arg \max_{c \in C} \dfrac{ P(d \mid c)P(c) }{P(d)} $$ It has been said in ...
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Derivation of an probability expansion used in Word2Vec classifier model

We are using the following notations, for this question, to calculate the probability values \begin{array}{|c|c|} \hline \text{$w$} & \text{target word embedding vector} \\ \hline \text{$c$} &...
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35 views

Is it ok to have an accuracy of 65% and a sensitivity of 90% with Naive Bayes for sentiment analysis?

I am creating a sentiment analysis model using Naive Bayes. When I test the model, I get an average accuracy of 65%; however, the sensitivity of the model is much higher, 90%. So, I am wondering if ...
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How to add pseudocounts to naive Bayes classifier?

I'm trying classify a document containing words $[x_i...x_n]$ as spam or non-spam, using this equation from Wikipedia: $$p(spam|x_i,...,x_n) = p(spam)\prod^n_{i=1}p(x_i|spam)$$ But I'm confused about ...
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How to make specific test data prediction with fitted GaussianNB Classifier in Python

I'm trying to make news classification. Here is the neural network: ...
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Are there any examples of state-of-the-art NLP applications that are still n-gram based and use Naive Bayes?

As far as I can tell, most NLP tasks today use word embeddings and recurrent networks or transformers. Are there any examples of state-of-the-art NLP applications that are still n-gram based and use ...
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How can the expectation-maximization improve the classification?

I am learning the expectation-maximization algorithm from the article Semi-Supervised Text Classification Using EM. The algorithm is very interesting. However, the algorithm looks like doing a ...