# 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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### My roc is low while precision and recall are high.Why is it so?

I bulit a naive bayes classifier from 60k vectors of text and each of the text is a 2000 dimension vector(Used bag of words for vectorization). Used simple cross validator to find the best alpha and ...
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### When will Naive Bayes misclassify continuous training instances?

For the discrete case, we can say that Naive Bayes might misclassify training data due to things like the zero-frequency problem. Why might Naive Bayes misclassify continuous training data?
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### Naive Bayes where posterior almost always equals prior (in MATLAB)

I have trained a naive Bayes classifier in MATLAB using fitcnb (description link) and 11 variables, seven of which are numeric (normal) and four of which are categorical ("mvmn" distribution name). I ...
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### Why does Naive Bayes work better when the number of features >> sample size compared to more sophisticated ML algorithms?

According to this article Because of the class independence assumption, naive Bayes classifiers can quickly learn to use high dimensional features with limited training data compared to more ...
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### Should PCA be (always) done before Naive Bayes classification

According to Wikipedia page on Naive Bayes: .. Naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence ...
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### Exploit grouped features for a classification problem

I've got a dataset of 100.000 labelled vectors, each one with a set of 100 binary features: $$F = \{ f_{1}, f_{2}, ..., f_{100} \}.$$ I am able to build a Bernoulli Naive Bayes classifier for ...
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### Review 5-stars Multiclassification Model

So I'm super new to DataScience World. And I'm Trying to do a TextMining Work. My goal is by reading user's reviews to predict their rating to a tech product. Problem? Multiclassification model with ...
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### Trouble understanding Bayes Theorem

I was watching a video on YouTube and i am not sure if the given solution is correct. Can someone confirm?
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### Proving that Gaussian Naive Bayes Decision Boundary is Linear

I need to come up with a Proof that Gaussian Naive Bayes has a linear decision boundary (In this case for Y={0,1}) I tried to work it out, but I am not able to pull out the xi term as it is stuck in ...
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### Predicting a combination

Question Suppose we have a training set of families. Where each family is defined as such… Family: A list of integers. Each integer is the age of one of the family members. (e.g. with a 45 year old ...
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### Does 'Conditional Independence' means there should be no multicollinearity among features?

I was reading the Naive Bayes article on Wikipedia and I read that, In Naive Bayes, the naive assumption that Naive Bayes make is "each feature is conditionally independent of every other feature, ...
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### Threshold to build confusion matrix?

I a have data set with 10 sections of data and each section shows one day observation. I designed the training and test set as follows: 8 sections for training the data and the last two sections for ...
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### Naive Bayes Assignment of Feature Probability

I'm using the .show_most_informative_features() function from NLTK's Naive Bayes to generate features to be used with a lexicon. In the case of my binary-classification problem, these features are ...
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### K-fold cross validation without randomness [closed]

For my research purposes, I am trying to eliminate the randomness in k-fold cross validation. My goal is to conduct cross validation where the first 10% from the dataset is the first fold so that the ...
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### In naive bayes, does the independence assumption hold only during marginalization?

I am trying to understand Naive Bayes. One of the principles of this method is to assume independence across features in the datapoints. Given this assumption are two distinct features independent ...
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### Grid based piecewise-stationary Poisson process test

I'm trying to fit a set of data to a variety of Poisson-based models, and have hit a stumbling block when trying to fit a piecewise-stationary Poisson process. What I mean by this is a Poisson process ...
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### use test data set after 10 Cross-validation

I applied 10 Cross-validation but I am a bit confused, I am not sure what is a correct way. 1- Should I apply 10 Cross-validation on all dataset, divide it into 10 folds and sum all the 10 matrices ...
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### Accuracy increases on decreasing the percentage of training data with stable precision, recall and F-score

I am currently working on a classification problem using tf-idf and Naive Bayes for two classes A and B. I have randomly shuffle the dataset before implementation, and I was experimenting with the ...
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### Can someone explain why the multinomial naive bayes classifier is linear?

I don't see how it is possible to write this as a linear function, yet it is said that it is. Can someone please explain how this is possible?
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### Naive Bayes - calculation error?

I am trying to do a simple Naive Bayes classification, but I am getting a probability greater than zero. What am I doing wrong? I have included my calculations below. Step 1: Prior. Calculate the ...
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### The update of probability distribution given evidence

I've got a problem, which I feel is related to this post. However I cannot really grasp the similarity and adopt it. I am trying to deploy a model for object detection from video. The model outputs (...
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### Is it feasible to perform a classification with one feature and Naive Bayes?

My question concerns the feasibility of this classification theoretically and realistically. I mean, if the best performance has been obtained by using only one feature, and adding features degrades ...
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### What attributes to apply laplace smoothing in naive bayes classifier?

I am reading naive Bayes classifier from the book "Data mining practical machine learning tools and techniques". The example of naive Bayes is given using the below dataset. As (Outlook=Overcast | ...
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### Tree Augmented Naive Bayes Probability of Attribute Conditioned on Parent and Class

Although this is a similar question, Factored Joint Distribution of Tree Augmented Naive Bayes Algorithm, I need additional clarification. I have training data for two different classes, so it is ...
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### Feeding Naive Bayes classification model background statistics prior to fitting

I have a few thousand tagged documents that I use to train a Naive Bayes text classifier (using sklearn in this case). In addition to the tagged documents, I have ...
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### gaussian naiive bayes with missing values?

I've got some data that I one-hot encode, A is numbers B is categories, C (the thing that I predict) is categories, so... ...
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### Naïve Bayes Theorem for multiple features

I understand the basic principles for the naïve bayes classification with one feature: $$P(Class|feature) = (P(f|Class) * P(Class)) / P(f)$$ We have a dataset that has the following attributes/...
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### The purpose of threshold in naive bayes algorithm

Suppose we have 2 classes. By using naive Bayes classifier we get posterior probabilities P(C1|D) and P(C2|D) for query sample.....suppose P(C1|D) is higher than P(C2|D), then we assign the tag of ...
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### Confidence of Probability Estimate

What is the difference between increasing the probability estimate a and increasing the confidence of the probability estimate
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### Which classifiers do consider the order of the features?

In case the order of features can make a difference in the results of a classification approach, which classifier algorithms perform better? I know Naive Bayes/KNN use bag of words and ignore the ...
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### Naive Bayes model diagnostics — testing independence between features

One of the main assumptions of the naive Bayes model is that the features are independent. This allows probabilities to be estimated. However, often times it is understood that this assumption doesn't ...
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### Reversed Naive Bayes - likelihood and parameter estimation

What happens if we flip the arrows in a Naive Bayes classifier? To clarify - from what I have found naive Bayes is defined for the following network structure: I'm interested to understand what ...
I'm interested in computing the Bayesian Information Criterion (BIC) for a set of Naive Bayes models. The NB can be described as follows, for a two-class $Y \in {0,1}$ with predictors \$X = (x_1, x_2, ...