# 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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### Cross-validation using when using the Gaussian Naive Bayes model [closed]

Well, I am trying to solve this clustering problem that involves the Gaussian Naive-Bayes algorithm. Question: Classification Consider the data in the file - link below. Train the algorithm Gaussian ...
50 views

### Given A and B, C are independent of each other. Given A, B and C, D and E are independent of each other. What is the minimal number of parameters?

Assuming all variables $A, B, C, D,$ and $E$ are random binary variables. I come up with Bayes net: $D \rightarrow B \rightarrow A \leftarrow C \leftarrow E$ which has the minimal number of parameters ...
1 vote
51 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 ...
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### 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 ...
115 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, ...
105 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: ...
56 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 ...
28 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 ...
51 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 ...
1 vote
73 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 ...
1 vote
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### 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 ...
104 views

### 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 ...
299 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, ...
167 views

I have found various references describing Naive Bayes and they all demonstrated that it used MLE for the calculation. However, this is my understanding: $P(y=c|x)$ $\propto$ $P(x|y=c)P(y=c)$ with $... 2 votes 0 answers 46 views ### My Gaussian Naive Bayes classifier is too slow I am trying to build a film review classifier where I determine if a given review is positive or negative (w/ Python). I'm trying to avoid any other ML libraries so that I can better understand the ... 3 votes 1 answer 91 views ### Why is my calculation of the probability of an object being in a certain class incorrect? In the attached image there is the probability with the Naive Bayes algorithm of: Fem:dv/m/s Young own Ex-credpaid Good ->62% I calculated the probability so: $$P(Fem:dv/m/s \mid Good) * P(Young ... 2 votes 1 answer 147 views ### Why do I get small probabilities when implementing a multinomial naive Bayes text classification model? When applying multinomial Naive Bayes text classification, I get very small probabilities (around 10e^{-48}), so there's no way for me to know which classes are valid predictions and which ones are ... 4 votes 2 answers 120 views ### Why would LDA have performed much better than SVM and Naive Bayes in diagnosing ADHD? In a final project in diagnosing Attention deficit hyperactivity disorder (ADHD) using Machine Learning, we obtained parameters from real patients. We used this data and got much higher success rates ... 6 votes 1 answer 693 views ### Why is the denominator ignored in the Bayes' rule? The naïve Bayes' generative algorithm is often represented by the following formula:$$\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 ... 5 votes 1 answer 740 views ### Is logistic regression more free from the conditional independence assumption than naive Bayes? To my understanding, logistic regression is an extension of naive Bayes. Suppose$X = \{x_1, x_2, \dots, x_N \}$and$Y = \{0, 1\}$, each$x_i$is i.i.d and$P(x_i \mid Y=y_k) \sim \mathcal{N}(\mu, \... 