# 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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### Why would LDA have performed much better than SVM and Naive Bayes in diagnosing ADHD?

In a final project in diagnosing Attention deficit hyperactivity disorder (ADHD) using Machine Learning, we obtained parameters from real patients. We used this data and got much higher success rates ...
1answer
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 ...
1answer
39 views

### 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 ...
0answers
22 views

### 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{$c$} &...
1answer
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, ...
1answer
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 ...
1answer
472 views

### Why is the denominator ignored in the Bayes' rule?

The naïve Bayes' generative algorithm is often represented by the following formula: $$\text{argmax}_{y} p(y|x) = \text{argmax}_y \frac{p(x|y)p(y)}{p(x)} \approx \text{argmax}_y p(x|y)p(y)$$ Why do we ...
0answers
15 views

### 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 ...
1answer
77 views

### 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, ...
2answers
3k views

### What are the main differences between a perceptron and a naive Bayes classifier?

What are the main differences between a perceptron and a naive Bayes classifier?
0answers
94 views

### $\frac{P(x_1 \mid y, s = 1) \dots P(x_n \mid y, s = 1) P(y \mid s = 1)}{P(x \mid s = 1)}$ 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: ...
0answers
11 views

### 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: ...
0answers
27 views

### 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 ...
0answers
22 views

### 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 ...
0answers
41 views

### 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 ...
1answer
64 views

### Understanding how to calculate $P(x|c_k)$ for the Bernoulli naïve Bayes classifier

I'm looking at the Bernoulli naïve Bayes classifier on Wikipedia and I understand Bayes theorem along with Gaussian naïve Bayes, however when looking at how $P(x|c_k)$ is calculated I don't understand ...
6answers
77 views

### What is the meaning of test data set in naive bayes classifier or decision trees?

What is the benefit of a test data set, especially for naive bayes estimator or decision tree construction? When using a naive bayes classifier the probabilities are a fact. As far as I know there is ...
1answer
150 views