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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multinomial Naive Bayes: how to calculate likelihood and posterior?

For a multinomial Naive Bayes model for $C$ classes and $D$ features, assuming $\theta \in \mathcal{R}^{C x D}$ is the matrix whose $\theta_{cj} $ element corresponds to $Prob(x_j = 1 | y= c)$, and $\...
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Can you help me to understand this deduction for proving Naive Bayes is a Linear Classifier?

In this tutorial on Naive Bayes Classififer in section 1.1, the author proved naive bayes is a linear classifier. Consider binary classification where $y=0$ or $1$. Our classification rule with ...
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Naive Bayes: Understanding the Entropy equation

I am trying to understand the entropy equation: -p1*log2(p1) - p2*log2(p2) - pn*log2(pn) Specifically why do we multiply each log by the probability? In the ...
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natural language processing analysis

I have selected SMS Spam Collection as my dataset for natural language processing task. I have done many pre-processing tasks on dataset such as removing punctuations, spell correction, stemming, and ...
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Does smoothing make any sense for Bernoulli Naive Bayes?

I'm trying to understand how the hyperparameters for the Bernoulli Naive Bayes in sklearn work for doing Randomized Search CV. If I use smoothing, and set ...
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Naive bayes losses accuracy/precisionthe bigger the sample

I'm currently using a naive Bayes approach but I ran into a problem regarding the data sampling: the larger the dataset - the worse it starts to perform, even with a training. My features are for the ...
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Comparison of classification algorithms: How can I interpret the results?

I am experimenting on a dataset of about 18,000 articles, 12000 tagged Fake and 6000 tagged Real. I'm building a fake news classifier and I'm comparing 4 classification algorithms: Multinomial Naive ...
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Using k fold cross validation gives lower results than without using it

I have implemented text classification in the sentence level by following through this tutorial. I have used tf-idf and NB & SVM as shown in the tutorial. The code is working fine with my dataset. ...
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Using Naive Bayes classifier for unsupervised learning

I was going through this article to learn about how the EM algorithm can be used to use the Naive Bayes algorithm for unsupervised learning. Suppose we have the following data without labels: 1 0 1 1 ...
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How to improve Naive Bayes?

I am solving a problem that address this question "What are the Actions that lead to high or low score?" I have the following Data that consist of text and score , I want to derive the words or ...
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How to calculate P(X1>X2) using naive bayes? [closed]

Hello I'm struggling with how to calculate P(X1≥X2) using the table and the Naïve Bayes from this question
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Comparing initial symptoms to a diagnosis

I have a small dataset of 80 patients with a diagnosis of 3 types of cancer: AML, ALL, and CML. They each come to the hospital with one or two symptoms: Fever, Diarrhea, or both. They all eventually ...
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Naive bayes classifier testing vector question

Lets assume that our binary class has five features x1,x2,x3,x4,x5 but the incoming test vector(x1,x2,x3,x4) has only four features .. How would we handle it in the Naive bayes classifier .. Do we ...
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Conditional Independence Example

Is there a canonical example of data which are conditionally independent? In other words, $X_1,\ldots,X_p$ are mutually independent given $Y$. This is the foundational assumption of the naive Bayes ...
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Naive Bayes - Additive Smoothing (or altering/dropping columns) when you get 0 probability

I had an exam recently where we had to train a Naive Bayes model on a given set. The dataset had a column which would give 0 probability for a YES value. I was told later that we were supposed to ...
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Naive Bayesian classifier calculation problem

I am having trouble calculating the Bayesian probability for this table, when doing naive Bayesian classifiers. When I try to calculate number 5 I get $1.25$ by doing the following: $P(a=1|id=1)= \...
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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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185 views

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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290 views

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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625 views

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 ...
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Multinomial vs Gaussian Naive Bayes Performance in Scikit Learn for Word Embedding Features

I am running some experiments using word embedding features with Multinomial and Gaussian Naive Bayes in Scikit learn. As far as I know, Multinomial Naive Bayes works on features with distribution ...
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Naive Bayes likelihood

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, ...

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