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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How to use K-fold classifier for comparison of different models
I am learning machine learning and went through a term K-fold cross validation.
I also took notes from this site to enhance my understanding.
As per the tutorial if it is 3 fold cross validation and ...
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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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Naive Bayes for data generation
NB is a classification method which according to Bishop's book is categorized in probabilistic generative methods. As far as I understood you can learn a join distribution from input-output pairs and ...
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Trouble reading bernoulli Naive bayes notation [duplicate]
Here is a mathematical description of the bernoulli naive bayes taken from the book ,Bayesian Reasoning and Machine Learning by David Barber
i want to know what does the notation below means ?
if ...
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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. ...
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Parameters in Naive Bayes
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 ...
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Modelling probability of continuous dependent variable from multivariate continuous independent variables
I am trying to create a model that predicts the probability of the size of a continuous dependent variable based on a number of (4) continuous independent variables. My dataset has 67 data ...
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Why use naive bayes instead of computing probability directly [duplicate]
Situation: In the exam question found below, we are tasked with using the naive bayes assumption to find: $$P[K = 1 | a = 1 \land b = 1 \land c = 0]$$
Problem:
Although I could solve the exercise, I ...
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naive bayes pseudo code and cleaning techniques
so let's say you are a librarian at a local library responsible for organising documents in 3 categories: new, fictions and scientific articles. one day, the library obtained access to some new ...
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How to count p(data) for Naive Bayes in the table below?
I'm trying to learn more about naive bayes + their calculation so I read the paper from Rennie et al, "Tackling the Poor Assumptions of Naive Bayes Text Classifiers". I'm curious about the ...
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Product of two normal distributions (for Bayes Rule) is not product of normal output variables?
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 $P(X|y)$ ...
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Logic of Sklearn Bernoulli Naive Bayes Classifier when the the predictors are not even binary?
I know the mathematics behind the Naive Baye's Bernoulli Classifier Algorithm and it is used to calculate the probabilistic results. As we know the Bernoulli Naive Bayes Classifier uses binary ...
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What is the prior probability in a Dynamic Naive Bayes classifier?
For a Hidden Markov process with multiple types of emissions, it is possible to perform current state classification using the Naive Bayes likelihood estimation:
$ p(j|b,d) \propto p(b|j) \cdot p(d|j) ...
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why doesn't naive bayes use the empirical distribution for continuous predictor by default?
Many standard implementations of naive bayes assume a gaussian distribution for the likelihood for continuous predictors. why doesn't it use the empirical distribution instead (or some kernal density ...
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Binary Classification with OpenNLP
I'm trying to apply binary classification with OpenNLP. I could already successfully classify movies by different genres. The data sample has the form:
...
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Function to sort a static list of items by recency and frequency
I'm working on a problem which requires me to sort a list of static items for each user. I understand best way to solve this problem would be to come up with a function that captures both the ...
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Does it make sense to use Gaussian Naive Bayes for a single feature?
I understand that 'Naive' Bayes refers to the approach where all the features are assumed to be independent. But I want to evaluate the performance of each feature individually before I combine all of ...
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Difference between Categorical Naive Bayes classifier and IBCC (independent bayesian classifier combination)
I am implementing an IBCC classifier to predict the probability of a given output class $y$ given some categorical inputs $X$. The paper I am usiing for this purpose is:
https://www.oxford-man.ox.ac....
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How to re-write with Bayes' Rule as a function?
I'm a little bit stuck. Say I have a P(A | B ^ C). How could I re-write this as a function with the terms P(B|A ^ C), P(B|C) and P(C|B) and can anyone explain how this was done?
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Naive Bayes - having trouble coming up with a case where Laplace smoothing changes the prediction
I'm thinking through the logic of Naive Bayes and encountered a brain teaser. I know that adding smoothing (alpha) to Naive Bayes can help to increase the accuracy of the model, which implies that it ...
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How to choose prior in Laplace Smoothing Naive Bayes?
Please check the image for reference
How to choose the prior probability of each feature .
Should we use the same prior for every feature or different features use different prior.
for example ...
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How to get features importance for different classifiers?
I am currently using different classifiers (Naive Bayes, Random Forest, SVM, Logistic Regression) and for some of them (e.g., MultiNaive Bayes) I cannot run some built-in function for feature ...
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How should I handle Laplace smoothing in Naive Bayes in this example?
I have a toy dataset on animals, with 4 features and 2 possible classes (mammals vs non-mammals).
I have summarized the dataset ...
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On the denominator of Bayes and Naive Bayes
There are many question on the topic but I think the comparison below is harder to find.
Let us assume that all variables in this example are binary for the sake of simplicity. The typical academic ...
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Why does Multinomial Naive Bayes work well on discrete features?
I understand Multinomial Naive Bayes is a specific instance of Naive Bayes when the data distribution is assumed to be multinomial.
In the sklearn documentation for Multinomial Naive Bayes, it is ...
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Logistic Regression and NaiveBayes with infinite dataset
I'm comparing the LR and NB performance on different datasets. And suddenly I am wondering what if we have a big dataset that is infinitely large (at least ensure both models trained to its best??). ...
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Prediction Model for Naive Bayes Multi-Class Classifiers
I've been using Naive Bayes for multi-class classifications, but I'm curious what's actually happening mathematically. I have had difficulty finding a straightforward mathematical explanation online. ...
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Performing Naive Bayes Calculations with Continuous Features
I am working on this homework problem and am not sure how to handle continuous features in a Naive Bayes classifier. I know the outputs for the categorical variables are simple to compute, for example:...
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What does it mean to train a Naive Bayes classifier for categorical features?
I know that Naive Bayes classifiers can be trained for both categorical and continuous (using a Gaussian distribution) features. I am less certain of how these two classifiers would differ. What does ...
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Performance loss after applying SMOTE
I'm working on a classification problem, and I've an unbalanced dataset, so I applied SMOTE algorithm in order to balance it.
While I got an increased performance when working with classification ...
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Why do we need to apply Laplace smoothing to all the words in Naive Bayes for text classification?
I understood that we need to apply for Laplace smoothing to the words that are not present in our training data. But then why/what is the need to do Laplace smoothing for all the words (even the words ...
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Marginal Likelihood in the Bayesian Posterior Formulation
What do we mean by "Integrating out the parameters" in Marginal likelihood? Particularly in the posterior formula.
The marginal likelihood in a posterior formulation, i.e P(theta|data) , as ...
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On Discriminative vs. Generative classifiers paper question
I have a question about the paper "On Discriminative vs. Generative classifiers: A comparision of logistic regression and naive Bayes" regarding the interpretation of the results. The ...
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How to approach non-deterministic synthetic event generation?
Context:
I'm working on a problem to generate hurricanes, earthquakes, and the like for a video game on a semi-realistic time scale. I probably want to amplify the number of events generated a bit ...
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Relation between the Naive Bayes Classifier and GAM
Problem:
This problem is about establishing a connection between the Naive Bayes Classifier and GAM. Consider a
classification problem with J classes. Let $f_j (X), X ∈ ℝ^p$, be a density function for ...
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How can I apply naive Bayes classifier for three classes (Positive, Negative and Neutral) in text data?
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, ...
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Confusion about 1- vs 2-tailed tests for feature selection by hypothesis testing
Suppose $x_i\ (i=1,2,...,N)$ be attribute values for $N$ samples from
class $W_1$ with mean $\mu_1 $ and $y_i\ (i=1,2,...,N)$ be attribute
values for $N$ samples from class $W_2$ with mean $\mu_2 $.
...
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How do I quickly calculate a Bayes classifier?
With the data from this post I want to quickly answer the following exam question:
Given that it is rainy, not windy, the temperature is hot and humidity
is normal, should you play golf or not?
Show ...
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The probability is proportional to the probability that the message is normal?
I was watching this Youtube video on Naive Bayes. The creator begins by using the example of an email spam filter to illustrate how Naive Bayes works. At 5:47, the narrator says that, technically, the ...
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How to Choose Class for Naive Bayes Classifier with Same Posterior Probabilities?
For Naive Bayes classifier in multiple class, I know that we had to choose a class with the highest posterior probabilities. I'm doing project using Naive Bayes classifier method and when i calculate ...
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Why does using conditional random field avoid independence assumption
I am reading about conditional random fields in Daphne Koller's book on probabilistic graphical models. One of the advantages to using CRF is that we can avoid modelling the correlations between ...
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How is this a "Bayes classifier"?
I am currently studying the textbook Learning with kernels: support vector machines, regularization, optimization and beyond by Schölkopf and Smola. Chapter 1.2 A Simple Pattern Recognition Algorithm ...
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naive bayes classifier with Exponentially distributed likelihood with big parameter
Just for the practice of it, I'm trying to do a naive Bayes classifier for data which has exponential distribution for the likelihood function, i.e. $X_k=x|Y=1 \in Exp(\lambda_k)$ where $k = 1,..., p$ ...
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Should I normalize the data? [duplicate]
I have four int columns with two of them having a value in 10s, and the other two have it in 100s. Should I, for the ease of applying the following algorithms, normalise the data, or would it not have ...
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$\frac{P(x_1 \mid y, s = 1) \dots P(x_n \mid y, s = 1) P(y \mid s = 1)}{P(x \mid s = 1)}$ indicates that naive Bayes learners are global learners?
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:
...
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Applying Bayes rule in the context of reinforcement learning [duplicate]
I was watching this video on reinforcement learning. At 1:28, it says following:
$$Pr(s'|a,z,s)=\frac{Pr(z|s',a,s)Pr(s'|a,s)}{Pr(z|a,s)}$$
I was unable to get how this was obtained. I pondered a bit ...
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In which cases GNB is worse than logistic regresion?
I am training and testing two models on the same dataset: a logistic regression and a gaussian naive bayes (sklearn's with the default parameters).
The dataset is the ...
