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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Scenarios where logistic regression gives much higher AUC than naive Bayes

It is my understanding that in the absence of covariation among predictor variables, logistic regression and naive Bayes should give nearly equivalent results with respect to predictive accuracy (as ...
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Is distributions defining necessary for a DAG to be a causal bayesian network?

First, let's define the following abbreviations: Directed Acyclic Graph (DAG), Bayesian Network (BN), Causal Bayesian Network (CBN), Conditional Probability Table (CPT), Conditional Probability ...
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Using Predictive Value Confidence Intervals to "Predict" Outcomes

Here's the quick version: Say I have a confusion matrix with the following data based on a proficiency cut score on a pretest and outcomes on (passing/failing) a class. The cut score was determined ...
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Is multinomial naive Bayes classification not naive Bayes classification?

Suppose I am thinking about a classification problem and I have my features $X = (X_1,...,X_n)$ and my classification $C$ (taking values in some finite set of classes $c_1,...,c_k$). The naive Bayes ...
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How to interpret plots of naive bayes classification model using naivebayes library in R

I'm using the 'naivebayes' library in R to run a three-label multiclassification model. When I plot the model, I get a series of charts--one for each predictor--that look like the following:enter ...
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Naive Bayer Classifier - Do we use dependence structure?

If we apply Naive Bayer Classifier and predict an unseen observation just by using the posterior probability calculated with Bayes theorem combined with the naive feature independence assumption, do ...
1 vote
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Determining a mix of clusters by using naive Bayes classifiers

I have the following question which I cannot seem to find an easy answer to: given that we have two groups (let's call them 1 and 2), is it possible to determine a mixed percentage of the groups by ...
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Why do we add 2 to the denominator when doing laplace smoothing? [duplicate]

Every explanation of laplace smoothing for, e.g, spam filtering, includes the following: The solution is to never let any word probabilities be zero, by smoothing them upwards. Instead of starting ...
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Naive Bayes classification for multivalued marginal

x y z C 1 0 1 1 1 1 1 1 0 1 1 0 1 1 0 0 1 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 The dataset in the table above consisting of boolean variables x, y and z and a single boolean output variable C. I ...
1 vote
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Probability of an image containing a specific object, by combining the results of multiple dependent tests

I am trying to assign to images the probability of them containing a metal building. The images can contain either metal buildings, non-metal buildings or no building. Let $B$ be the event that an ...
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Naive Bayes with R - ISLR Default dataset

I'm currently reading chapter 4 of An Introduction to Statistical Learning by James et alter. I've been trying to go through the examples myself, replicating the calculations in R. Section $(4.4.4)$ ...
1 vote
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Proving consistent/inconsistency of a fusion of KF estimates

I have a distributed fusion scenario with a single target where two sensor nodes $i,j$ estimate the true state $\mathbf{x}$ using a local Kalman filter. The (linear, Gaussian) measurement errors of ...
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What is the extension of Naive Bayes that breaks the conditonal independence?

I had the idea that we can overcome the conditional independence of features within Naive Bayes classification by assuming that we have latent (hidden) sub-classes. Let me explain. For example, if we ...
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Naive Bayes is a "special case" of logistic regression - which other models?

Suppose $Y \in \{0, 1\}$ is a response variable and $X = (X_1, \cdots, X_p)$ are covariates with $X_j \in \{0, 1\}$ for each $j = 1, \cdots, p$. In the Naive Bayes model, we assume conditional ...
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In Naïve Bayes, why do we estimate Pr(W|H)*Pr(H) instead of Pr(W)

This wikipedia article describes spam filtering using Naïve Bayes: https://en.wikipedia.org/wiki/Naive_Bayes_spam_filtering It says P(S|W) is given as ...
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What type of classifier is Naive Bayes?

I am currently studying the Naive Bayes method (a classification method) and I am having quite some trouble classifying it as a hard or soft classifier. Below follows a quick introduction on the ...
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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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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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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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NaiveBayes in R - Understand importance of Variables

I am working with a data set where the response variable is binary and 15-20 continuous and categorical variables. I am using the naiveBayes library to compute the model. I am interested in ...
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How to handle missing values NaiveBayes Scikit Learn

I am working with a dataset which has 34 features (numerical, nominal) and the target class. Several of the columns have missing values, especially one column has approximately 50% missing values. I ...
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Why are linear/logistic regression and naive bayes called "parametric" while SVM, random forests, neural nets are not? [duplicate]

This table is mentioned in What algorithms need feature scaling, beside from SVM? It says that linear regression, logistic regression, and naive bayes are parametric, while KNN, decision trees, ...
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Impact of Laplace smoothing on likelihood in Naive Bayes

When 1 is added to word count in Laplace Smoothing in Naive Bayes, the new probabilities either increase or decrease as shown below. Though the problem of "zero" probability has been solved. ...
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Use Naive Bayes to label unlabeled data

I have an Excel file that includes all product information (web scraped from Zalando) of 10k dresses. So for each dress/line I have multiple features available (brand, color, neckline, length...) I ...
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Naive-Bayes Iris R, Correct Implementation? [closed]

So I am trying to understand the naive Bayes classifier by implementing it in R. However I'm not sure if my implementation is correct. Using the iris dataset and Sepal Width / Length as features. ...
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Log Naive Bayes NLP dropping the denominator [duplicate]

I'm learning about the the Naive Bayes classification and I don't get what the squiggly alpha sign means and what it means that "Denominator remains constant for given input." Is it because ...
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Computing a prior from two components in Naive Bayes

Given a model parameter $\theta$ that is composed of two distributions in a Naive Bayes classifier, how is $P(\theta)$ typically computed in practice? More specifically, from the article of Nigam et ...
1 vote
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Laplace Smoothing in Naive Bayes [duplicate]

I'm reading up on Laplace Smoothing/Add-1 Smoothing in Naive Bayes and I'm given the formula $\frac{Count(Feature=Value) + α}{N + α\cdot k}$. In reference to the image above, if we have to classify ...
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Adaboost, using weak learners as Gaussian Naive bayes, has oscillating/unpredictable training error as we increase the number of weak learners. Is there a specific reason for this? Y-axis is the ...
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What attributes does Laplace Smoothing apply on in Naive Bayes

Consider the dataset: Outlook Temperature Humidity Play Golf? Overcast Cool Low Yes Sunny Hot Low Yes Rainy Cool High No Sunny Hot High No Rainy Cool Low Yes There are 3 possible values for the ...
1 vote
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How do you interpret the matrix confusion in this Naïve Bayes output?

Why are my correctly classified instances lower than incorrectly classified? This was tested using Naive Bayes with option testing Cross-Validation set at 10 folds. Here is the image of the results: ...
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How to calculate the Bayesian Risk Classifier

I'm not exactly sure how to calculate the Bayesian risk Classifier $L(r^*)$ for $Y\in\{ 0,1 \}$. For this scenario, assume: $X\in\mathbb{X}=[0,1],Y\in\{ 0,1 \}$ $\pi_y=P(Y=y)=1/2$ for $y\in{0,1}$ ...
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Stuck on a step calculating Naive Bayes Classifier,

Using the example at 3Blue1Brown I constructed a table to help me remember Bayes theorem where L=Librarian and S =Shy. I understand that $$P(S,L) = P(S|L)P(L) = P(L|S)P(S) = \frac{4}{210}$$ I am ...
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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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