Questions tagged [naive-bayes]

Question 1

I'm reading about how Conditional Probability/ Bayes Theorem is used in Naive Bayes in Intro to Statistical Learning, but it seems like it isn't that "groundbreaking" as it is described? If ...

Question 2

Why is it better to treat the rating prediction of a text review as a regression problem rather than a classification one? Is it because the ratings (1,2,3,4,5) are ordinal variables? What kind of ...

Question 3

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 ...

Question 4

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 ...

Question 5

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 ...

Question 6

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, ...

Question 7

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: ...

Question 8

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 ...

Question 9

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 ...

Question 10

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 ...

Question 11

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 ...

Question 12

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, ...

Question 13

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 $...

Question 14

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 ...

Question 15

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 ...

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Questions tagged [naive-bayes]

Doesn't every single machine learning classifier use conditional probability/Bayes in its underlying assumptions?

How to predict the rating of a text review and improve it?

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?

How would the probability of a document $P(d)$ be computed in the Naive Bayes classifier?

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

How can I apply naive Bayes classifier for three classes (Positive, Negative and Neutral) in text data?

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

Why don't ensembling, bagging and boosting help to improve accuracy of Naive bayes classifier?

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

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

Why do Bayesian algorithms work well with small datasets?

What to do when PDFs are not Gaussian/Normal in Naive Bayes Classifier

What is the relationship between MLE and naive Bayes?

My Gaussian Naive Bayes classifier is too slow

Why is my calculation of the probability of an object being in a certain class incorrect?

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