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".

learn more… | top users | synonyms

0
votes
1answer
11 views

Naive Bayes using 1d features vs one n-D features with Kernel Density Estimation - Independence assumption

Given a set of features $x_1,x_2,x_3, ... \in \mathbb{R}$ and output class variable $y \in \mathbb{R}$ I could do Naive Bayes using the independence assumption of $x_1, x_2, x_3, ...$ to predict the ...
2
votes
1answer
68 views

Analogy between Neural network and naive bayes

I am trying to understand the analogy between a single layer neural network and naive Bayes classifier. Particularly, I want to know if, in a neural network, the variables are independent given the ...
0
votes
0answers
19 views

How will I explain difference between multivariate bernoulli and multinomial event model of Naive Bayes to my grandmother?

I read that multinomial event model that it performs better in comparison to multivariate Bernoulli event model for text classification. What is the concept behind these event model.? How will I ...
3
votes
2answers
68 views

How does Naive Bayes work with continuous variables?

To my (very basic) understanding, Naive Bayes estimates probabilities based on the class frequencies of each feature in the training data. But how does it calculate the frequency of continuous ...
0
votes
1answer
33 views

Bayesian networks and weird probabilities

I have to solve the following problem: Suppose we have a bayesian net in which we have the following variables: R, PA and PR Let: P(R) = 0.1, P(PA) = 0.5, P(PR|R, PA) = 0.6, P(PR|¬R, PA) = 0.4, P(...
0
votes
1answer
26 views

Does Naive Bayes assume normality?

I came across this paper about Naive Bayes that states [Naive Bayes] is based on another common simplifying assumption: the values of numeric attributes are normally distributed within each class. ...
0
votes
1answer
40 views

Naive Bayes Derivation

I was going over the derivation of Naive Bayes, and the following 3 lines were given: Suppose $X = \left < X_1, X_2 \right>$ \begin{align} P(X|Y) &= P(X_1, X_2 | Y) \\[2pt] &= ...
0
votes
1answer
28 views

Any way of getting vector of probabilities for each response with Naive Bayes in R?

I'm currently using naiveBayes from {e1071}. My response is simply a prediction based on my independent variables. Is there a ...
1
vote
0answers
24 views

What is the best form (Gaussian, Multinomial) of Naive Bayes to use with (one-hot encoded) features?

I've been asked to use the Naive Bayes classifier to classify a couple of samples. My dataset had categorical features so I had to first encode them using a one-hot encoder but then I was at a loss ...
0
votes
0answers
29 views

Difference between naive Bayes / multinomial / Bernoulli / SVM

i have a question to what consider the Naive Bayes algorithms, i am confused about the difference between the 3 algorithms: 1)The original Naive Bayes, 2)Bernoulli Naïve Bayes, 3)Multinomial Naive ...
3
votes
1answer
33 views

Using the Naive Bayes classifier in R with continuous variables

I am trying to predict a categorical variable (type of job, there are three classes) using a dataset that mainly consists of continuous variables (like years of education, salary,etc), using the Naive ...
1
vote
1answer
21 views

Differences between categorical classification algorithms

Given data where the class is categorical (finite and discrete), there are multiple ways to come up with a classifier. One could use multinomial logistic regression, or support vector clustering (...
0
votes
0answers
41 views

Machine learning approach when facing low predictive power features

My dataset has 3.6k samples and 600+ one-hot encoded features. Each feature has between 5-2000 instances, averaging around 150. Intuitively, I don't believe that my features should have much ...
0
votes
1answer
27 views

The difference between the Bayes Classifier and The Naive Bayes Classifier?

I'm trying to find the connection between both classifiers. In NBC we assume that all the features are independent of each other so we can calculate the posterior probability easier. I assume Bayes ...
1
vote
1answer
180 views

Normalize non-normal distribution?

I have a query regarding a comment I found, which will surely shed some light. In this article: http://www.analyticsvidhya.com/blog/2015/09/naive-bayes-explained/ I found: If continuous features ...
0
votes
0answers
9 views

Naive Bayes and smoothing

For simplicity, let's say that we want to perform binary classification using Naive Bayes on a Boolean function. That is, the target function is $c: \{0, 1\}^n \rightarrow \{0, 1\}$. Hence, the two ...
1
vote
2answers
39 views

Is Naive Bayes suitable for large datasets with thousands of features?

I have a data set with 100 million rows and 15,000 categorical variables each with 0/1 values. My target variable is also a 0/1 binary variable. Is Naive Bayes suitable in terms of computational ...
0
votes
1answer
47 views

true negative is 0% whereas true positive is 100% correctly classified

I used Naive Bayes from Spark's MlLib to train a model and test it on the data (in the form of an RDD). The results were confusing. the data and results are as follows: The problem is a binary ...
0
votes
0answers
23 views

Simple Bayesian Classifier for spam detection

I am a very beginner at machine learning, and I'm reading a book about it. I came across some lines of code in R for naive bayesian classification for spam detection. This is the code: ...
0
votes
1answer
40 views

Naive Bayes: Intuition behind the Evidence

I understand how to use NB and have used it often. However, I am trying to understand how the two different ways I use to calculate the evidence (P(E)) result in ...
0
votes
0answers
28 views

Naive Bayes + k- fold Cross Validation

How can I find the mean and standard deviation of the accuracy of k-fold cross validation when the classifier method is Naive Bayes?
0
votes
0answers
45 views

How to compute probability

I have a dataset consisting of 4000 observation from each 324 continuous features are extracted. Each observation has been labeled a class. Since each feature from that dataset is continuous, have I ...
0
votes
0answers
31 views

how do i compute the probability [duplicate]

I have a continous dataset consisting of 4000 observation from each 400 features are extracted. Each observation has been labeled a class. Since the dataset is continous, have I created a distribution ...
1
vote
0answers
42 views

Is the dataset Normally distributed?

So based from this link http://www.simafore.com/blog/bid/107702/2-ways-of-using-Naive-Bayes-classification-for-numeric-attributes I began to realize it might be a good idea to compute the pdf of ...
0
votes
0answers
14 views

Would it make sense to train a naive bayes model with PCA compressed data?

I would like to improve my result from my training, from which i was thinking maybe PCA could be able to help determine which features are useful for my classification task. But would that help?
1
vote
0answers
21 views

Create a model based on the distribution of data for classification purposes

I have data set which is stored as a matrix where each row is an observation (number of observations listed is 4000 ) and each column the feature extracted from that observation (number of features ...
0
votes
0answers
24 views

why am i getting bad predictions rates?

I am trying to make a classifier capable of recognizing digits using the naive bayes method. Problem is though that i am getting pretty bad results. I thought the reason would be because of the ...
0
votes
0answers
22 views

How to interpret the the train result?

I using the caret trained my dataset using naive bayesian as method with an repeated 10-fold cross validation. I seem to get a lot of different output, but can't ...
0
votes
0answers
15 views

How should i represent my data for a naive bayesian based classifier?

I am at the moment trying to find out how well the naive bayesian method works for classification purposes. The data I am having is hand written characters ("A", "B","C", "D"). My dataset is stored ...
4
votes
1answer
63 views

How was this intergral derived from Bayes' Rule in David Heckerman's Bayesian Network paper?

I am trying to follow this paper titled "A Tutorial on Learning With Bayesian Networks" by Microsoft researcher David Heckerman. In it I am unable to figure out how he got to Equation 2 from ...
0
votes
0answers
16 views

Weka Experimenter Tool (xx/yy/zz) explanation

I am using Weka experimenter tool and I need help to fully understand how this count works. I found this explanation in a paper: The annotation v or * indicates that a specific result is ...
0
votes
0answers
30 views

What is the meaning of laplace, eps and threshold in NaiveBayes package in R e1071 lib?

I am using NaiveBayes for text classification, I am interested on tagging a text (like a blog post). What I am finding is that normally I have results in which a tag has a probability of 0.9999 of ...
2
votes
0answers
40 views

How to extract the predictions and probabilities of each training sample in a cross-validation result in caret (R)?

I'm learning the caret package in R for classifications by Naive Bayes. I'm following the tutorial from: http://topepo.github.io/caret/training.html Thanks for the great tutorial! But I have one ...
0
votes
1answer
39 views

Does Naive Bayes( library:klar) in R calculates denominator of conditional probability while giving output?

Generally, when using Naive Bayes for classification, denominator is ignored as probability is directly proportional to the numerator as denominator is same for all the classes. So, I want to know if ...
0
votes
0answers
14 views

pairwise chi square test for independence on large df

I have a data frame (mydata) of discrete data (binned 1 through 10) where each column represents a variable I'd like to use in a Naive Bayes algorithm and each row represents a city throughout Europe. ...
0
votes
0answers
19 views

What algorithm would you use for this problem?

I want to compare two sets of data to predict if someone is going to get sick or not. (both datasets are from the same subject) Dataset 1 :The training set is a matrix (7 col x 14 rows) of daily ...
0
votes
1answer
41 views

Choosing number of samples to train a model

(On behalf of a colleague) I have performed some modelling based on a naïve Bayes classifiers model (weighted genomic risk score) and obtained reasonable ROCAUC results (used ROCR, pROC, and SDMtools ...
4
votes
2answers
148 views

“Good” classifier destroyed my Precision-Recall curve. What happened?

I'm working with imbalanced data, where there are about 40 class=0 cases for every class=1. I can reasonably discriminate between the classes using individual features, and training a naive Bayes and ...
0
votes
0answers
12 views

Help me inform multiple regression coefficients with additional dataset

I have a data set with 24 year on year observations. That is for a single company. I have around 500 companies with incomplete datasets (i.e. with missing values). I am trying to predict a Y ...
0
votes
0answers
30 views

Any additional suggestion to combine weights and probabilities in Naive Bayes to classify tweets

As a side project, I am trying to build a simple Naïve Bayes sentiment analysis model to classify the sentiment of some tweets as either positive or negative. But I am trying to incorporate sentiment ...
4
votes
1answer
36 views

Hypothesis space of Naive Bayes and kNN

I am confused about the hypothesis space of those two classifiers. In the case of linear regression, it's pretty straightforward ; the possible hypothesis are equations of lines, that is, linear ...
0
votes
2answers
78 views

Highly correlated features in text mining despite mutual information criterion

I'm trying to classify documents into two classes using the Bernoulli Naive Bayes algorithm, as described here in chapter 13. I've extracted 500 tokens (out of more than 30,000) from my sample ...
0
votes
0answers
27 views

Intuitive example for MLE, MAP and Naive Bayes classifier

I am trying to understand MLE, MAP and naive Bayes classifier, but it's difficult to understand the differences without some numerical example. Can someone give simple intuitive numerical example for ...
2
votes
2answers
45 views

Text categorization using Naive Bayes: Why isn't this working?

I'm trying to implement a system for text categorization using Naive Bayes as part of a school project. I have to hand code the algorithm and have been having some issues. To make sure I understand ...
6
votes
1answer
76 views

Algebraic classifiers, more information?

I have read Algebraic classifiers: a generic approach to fast cross-validation, online training, and parallel training and was amazed by the performance of the derived algorithms. However, it seems ...
1
vote
1answer
64 views

Why does Naive Bayes use gaussian pf rather than Student's t?

Every source in the literature I could find about naive Bayes mentions using a gaussian's probability density function, using the mean and variance estimated from the data itself. This strikes me as ...
0
votes
1answer
59 views

Very poor accuracy in Naive Bayes for ancestry/surname classification

Naive Bayes has a very good reputation on the classification of surnames by ancestry (see http://www.ncbi.nlm.nih.gov/pubmed/24944286). I would like to apply a Naive Bayes classifier in R to ...
0
votes
1answer
42 views

How to apply a fitted Tree-augmented Naive Bayes classifier to new cases

I am running Tree Augmented Naive Bayes algorithm in R and I have got the desired network. However, unlike logistic regression, Bayesian is non-parametric i.e. I do not have any coefficients which I ...
0
votes
0answers
11 views

Naive Bayes Bernoulli with more than 2 class labels?

I am a little confused about how to perform Naive Bayes Bernoulli model. In the first link, they split the class labels and the predictors. It is a binary class label here. But what if I do not have a ...
0
votes
0answers
68 views

Creating Naive Bayes Model for numerical data in R

I want to calculate the missing value using the NaiveBayes predictor. I am using a dataset with missing values at some rows, ...