# 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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### Equivalence of Logistic regression to Gaussian naive bayes

I was revisiting the differences between logistic regression and Naive Bayes, and had a conceptual question. A logistic regression classifier makes intuitive sense to me as a classifier that directly ...
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### prior & posterior probability in Bayesian Decision Theory

Learning Bayesian decision theory (specifically in Machine Learning) recently, couldn't figure out what do the posterior possibility $P(c|x)$ and the prior possibility $P(x|c)$ mean exactly. Anybody ...
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### Simplistic linear estimator for a probability vector

I am working on a problem where the unknown probabilities $p_i$ are related to observed rates/frequencies $\pi_\alpha$ as $$\pi_\alpha = \sum_iW_{\alpha i}p_i,$$ where $W_{\alpha i}$ is known (...
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### Naive Bayes fits multiple hyperplanes in the case of multiclass classification problems?

Naive Bayes differentiates feature distributions given target labels, and intuitively, it fits a hyperplane to the given data set. But I do not fully understand whether Naive Bayes fits multiple ...
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### Understanding the application of MLE in Naive Bayes

I was looking at the Naive Bayes classifier models (Binomial, Multinomial and Gaussian) and trying to understand the theory behind them a bit better, but am unsure if I understand the MLE approach ...
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### Population stability index and Text Data Length

I'm training a language detection model using: a training set, classified between English and not English sentences or small paragraps, where the length of the sentences can vary a score set that is ...
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### Assuming Independence in Naive Bayes

If features of the Naive Bayes are not independent then what are the consequences of the results?
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### Understanding rare definition of the likelihood function and corresponding posterior from research paper

Reading the paper https://storage.googleapis.com/pub-tools-public-publication-data/pdf/b20467a5c27b86c08cceed56fc72ceadb875184a.pdf i came across a rare definition of the likelihood function that in ...
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### interpreting confusion matrix results

I have a dataset on unemployed individuals enrolled in a job training program where I am trying to predict whether 6 months post-enrolment they 1) gain employment, 2) stay unemployed, or 3) drop out ...
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### If I engineer a new feature such that feature C = feature A/feature B, must I drop features A and B from a Gaussian Naive Bayes model?

As the question asks, is it bad data science not to drop the dividend and divisor features when creating a new feature that is their quotient when working with a Naive Bayes model? My understanding of ...
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### Is it possible for the gains line to fall below the naive rule in a lift chart?

I created a naive Bayes model and generated this lift chart . Is it possible that my model could underperform the naiveBayes rule?
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### AUC - Logistic Regression versus LDA, and Naive Bayes

everyone! I am a newbie on machine learning, and I am now interested on classification modeling. I used logistic regression, linear discriminant analysis (LDA), and naive Bayes on my notebook DataCamp ...
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### How many divergent transitions are too many?

I am running a Bayesian linear mixed effects analysis. Four chains for 3000 iterations. I end up with four divergent transitions. Is this too many or can I proceed? How do I know if it's too many? I'...
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### Base rate calculation for customer conversion

Question: What is the base rate of conversion for mobile versus desktop sites? Total no of customers: 590381 Out of 590381, the Total no of customers that were converted: 701 These customers used ...
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### Intuition for why LDA is a special case of naive Bayes

The naive Bayes classifier assumes the regressors to be mutually independent, while linear discriminant analysis (LDA) allows them to be correlated. James et al. "An Introduction to Statistical ...
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### Why is naive Bayes overconfident?

In the fourth edition of "Artificial Intelligence: a modern approach" by Russel and Norvig, they write in section 12.6, regarding the Naive Bayes Model for text classification, the following:...
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### How can Naive Bayes overfit the data?

I know that Laplace smoothing results in a high bias of Naive Bayes. If the value of the smoothing parameter (alpha) is large, then the probability distribution will be uniform for all the features. ...
This is from https://scikit-learn.org/stable/modules/naive_bayes.html In the last line it says and we can use Maximum A Posteriori (MAP) estimation to estimate $P(y)$ and $P(x_i|y)$; the former is ...
When we apply Bayes' rule in machine learning, we want to compute the posterior probability $P(y|X)$ by multiplying two probability distributions (the observed class-conditional likelihood $P(X|y)$ ...