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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Post-process the output of a Multinomial Naive Bayes text classifier

I have a multinomial text classification application where there are other features than the words in text which can be useful to do the classification e.g, contains email address, contains an URL, ...
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2answers
30 views

In Naive Bayes, why bother with Laplacian smoothing when we have unknown words in the test set?

I was reading over Naive Bayes Classification today. I read, under the heading of Parameter Estimation with add 1 smoothing: "Let $c$ refer to a class (such as Positive or Negative), and let $w$ ...
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24 views

How to handle unseen features in a Naive Bayes classifier?

I am writing a naive bayes classifier for a text classification problem. I have a bunch of words and an associated label: ...
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1answer
21 views

Greater than 1 Naive Bayes Probabilities?

I am trying to train a Naive Bayes classifier. In addition to getting the most likely class as an output from the Naive Bayes classifier, I would also like to compute the probabilities associated with ...
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9 views

Which classifier is the best for short text classification, Naive Bays or RBFN? [on hold]

I am doing my M.E. project on short text classification for Online Social Networks.I am using Weka for classification.Please tell me between Naive Bays and RBFN which classifier is the good one for ...
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Naive Bays classifier showing results for precision, recall and F value is always 1

I am implementing Naive Bays text classifier using Weka. I have trained it with very few words (about 20). I am getting the result that precision, recall and f value all as 1. Is this possible? ...
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10 views

Variable coarsening in Naive Bayes

Say we have a binary classification problem that we want to solve with Naive Bayes. All features are categorical variables. Say we focus on a single feature that takes one of $N$ possible values. If ...
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14 views

visualize the naive bayes with k-fold cv

I have done the classification using naive Bayes as a classifier, and applied 10-fold CV.I know that I can get the mean and variance of the result. However, how can I plot the classifier performance? ...
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1answer
60 views

Example of how the log-sum-exp trick works in Naive Bayes

I have read about the log-sum-exp trick in many places (e.g. here, and here) but have never seen an example of how it is applied specifically to the Naive Bayes classifier (e.g. with discrete features ...
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1answer
42 views

Gaussian Naive Bayes really equivalent to GMM with diagonal covariance matrices?

Murphy writes that a multivariate Gaussian used in a generative classifier ("Gaussian discriminant analysis"), i.e., $p(\mathbf x|y=c,\mathbf\theta) = \mathcal{N}(\mathbf x|\mathbf y_c,\mathbf ...
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2answers
43 views

Best feature selection method for naive Bayes classification

i want to make classification with naive Bayes. I have got about 100 Features. Numerical ones as well as categorical ones. Since i want only the most relevant ones to be included for the ...
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13 views

Conditional Probability With Naive Bayes

I have recently begun exploring R I have downloaded a data set that contains flight times for when a plane takes off at Origin and Lands at destination The data has up to three months’ worth of ...
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3answers
136 views

Naive Bayes: Imbalanced Dataset in Real-time Scenario

I am using scikit-learn Multinomial Naive Bayes classifier for binary text classification (classifier tells me whether the document belongs to the category X or not). I use a balanced dataset to train ...
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21 views

A Graphical Model for Fellegi-Sunter Record Linkage

I am trying to understand the Fellegi-Sunter Probability Model for Record Linkage problem. I am following the thesis at: http://www.inf.ed.ac.uk/publications/thesis/online/IM080663.pdf in order to ...
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1answer
23 views

Multinomial Naive Bayes performance with and without Bigrams/Stopwords

I have a dataset of 300 text documents that I have manually classified into three classes. The first two classes are relatively equal in size; the third class is about 40% larger than each of the ...
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40 views

How do we build a model with pyMC

I am a newbie programmer with pyMC. I have a model given as following $$\rho=\sqrt(x^2+y^2)$$ $$z=f(x,y)=\frac{c^3}{\log(c)+1}\frac{\log\rho}{\rho^2}+\frac{5.3}{r^2}\rho^{-3}$$ and I want to find the ...
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47 views

Using PyMC to obtain the posterior for the parameters of a parametric model?

I am a newbie with pyMC and I want to program an MCMC sampling for a complicated problem. I have a function given by in the following: $$g(r,c,\theta_1,\theta_2)=\frac{\delta_c}{\Sigma ...
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15 views

Are there any issues with the naive bayes classifier when the number of features are greater than the number of observations?

Say I have $f$ features and $n$ observations. If $f>>n$ are there any inherent issues that arise if a naive bayes classifier is used on such a data set? To be more concrete, let's say $f=200$ ...
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1answer
82 views

Naive Bayes with unbalanced classes

As a part of a project for the university is should train a Naive Bayes classifier to classify question and answers in three different categories, the task should be easy since that the 3 classes are ...
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4answers
42 views

Get rid of the influence of a predictor

I am currently working on a data mining project during an in internship, and I use among others methods decision trees. My problem is that I have a categorical predictor very influent on ...
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57 views

Overfitting naive Bayes

I understand naive Bayes is used largely in text classification. However, the number of features tend to outnumber the number of documents. Does this not result in overfitting where the number of ...
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2answers
31 views

Different number of samples affecting naive Bayes

I am trying to build a model of 7 classes text documents. However, i do not have equal number of samples for each class. I can have close to about 10k documents for class A but only about 100 ...
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109 views

Possible Problem in Naive Bayes Spam Filters

I realized that, never been observed words in training sets, decrease test probaility of documents being spam and not spam after i mentioned here and this link "... you need to account for it as a ...
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1answer
145 views

Naive Bayes non-Dictionary Term in Test Document

Using Laplacian Smoothing we can get rid of 0 probabilities if a term occur in spam and does not occur in ham class or vice versa. My question is about what if a term in test document does not occur ...
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23 views

What is delta in the maximization step in this EM algorithm?

The algorithm is used to classify english vs non-english tweets from unlabeled data. Given n observed tweets (x1 ... xn) where each tweet xi is a collection of d words (xi1 ... xid). y is the class ...
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60 views

Naive Bayes Python implementation differences

Am currently using Naive Bayes for a multi labelled text document classification problem. But I would like to know the differences (advantages and disadvantages) of using SkiLearn Naive Bayes or ...
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72 views

Machine Learning Algorithm Confusion

I made a small application about cricket prediction using Machine Learning. I took records of 10 years (2001-2011) of ODI matches and prepared a training set. Now to predict a win or loss for a ...
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1answer
49 views

Passing a single item and getting a prediction

I have completed a demo of a naive Bayes classifier for predicting whether an SMS message is spam or ham. My question is, how can I use this in a practical manner? It appears I would have to ...
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30 views

Why naive bayes classifier cannot perform correctly on 1 conditional probability?

I use e1071 package for naive bayes classification. My dataset is having 2 condition/feature (FF1 and FF2) to predict a class of signal (ELO or FP). When I used the following line of code: ...
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28 views

Why are Bayesian classifiers “robust to noise”?

In many different settings I've read that Bayesian classifiers like Naïve Bayes and Bayesian Networks are more robust to noise in the input data than other classifiers. I'm wondering what the evidence ...
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1answer
97 views

Naive Bayes: Continuous and Categorical Predictors

It's my understanding that most types of common classifiers (Support Vector Machine, for example) can take a mixture of categorical and continuous predictors. However, this doesn't seem to be true ...
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35 views

Probability of class membership given univariate normal distribution

Assuming a class is well described with a normal distribution of u and s, is it reasonable to calculate the probability of membership as: $Pr(x)=Pr(|x-u|)=2.0*\text{cdf}(|x-u|,s)$? I've briefly ...
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24 views

Obtaining and sampling from the posterior predictive of a naive Bayes classifier

I have trained a naive Bayes classifier with on a dataset with a dichotomous outcome and multinomial attributes (predictors). I managed to get a Maximum a posteriori (MAP) estimate which is good ...
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14 views

How to cross validate with optimal sample size selection?

My problem is as follows: I got a training set composed of texts, where 10% of observations are 1, and 90% are 0. To make it simpler, lets say that 1000 is 1, and 9000 is 0. I implement Naive Bayes ...
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1answer
135 views

Machine learning techniques for spam detection, and in general for text classification

I am going to configure a system for spam detection. What I have is a dataset of labeled (spam/not-spam) strings containing, mostly, sentences. I have a background in machine learning techniques, but ...
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27 views

Naive Bayes Classifier - measure accuracy after training

I have built a prototype Naïve Bayes Classifier in an Excel spreadsheet. My data is a transaction (an order) with 13 parameters. This translates directly to a feature vector of (feature_1, feature_2, ...
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53 views

Exploratory Analysis - finding the most important factor

I have a dataset of 113 variables. In exploratory analysis the first thing I want to know is what are the most important factors on a single variable (revenue). I learned that naive Bayes would ...
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1answer
47 views

How can I convert variable distribution parameters into training data for Naive Bayes classifier?

I am trying build a Naive Bayes classifier from data pulled from scientific papers. I want to use the reported variable distribution parameters to approximate a dataset which I can use to train the ...
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1answer
51 views

Simple question about multivariate/multiclass classification

From this link Text Classification using Naive Bayes, there are two models described for classification, Naive and Bernoulli. My question is if i want to make this classifiers for multiclass ...
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1answer
566 views

Accuracy rate in naive Bayes classification

I am trying to use a naive Bayes classification technique to predict fraudsters (Caller). My training set of 138 instances has 5 columns viz. ...
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190 views

Why does nobody use the Bayesian multinomial Naive Bayes classifier?

So in (unsupervised) text modeling, Latent Dirichlet Allocation (LDA) is a Bayesian version of Probabilistic Latent Semantic Analysis (PLSA). Essentially, LDA = PLSA + Dirichlet prior over its ...
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3answers
386 views

Naive Bayesian Algorithm in R/SAS for categorical input variables?

Could anyone please let me know how to implement Naive Bayesian Algorithm in R or SAS?I have got a training dataset with all the categorical predictors and target variable(3 levels).I got to build a ...
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1answer
360 views

Logistic Regression\SVM implementation in Mahout

I am currently working on sentimental analysis of twitter data for one of telecom company data.I am loading the data into HDFS and using Mahout's Naive Bayes Classifier for predicting the sentiments ...
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161 views

Sentimental Analysis using Naive Bayes

I am working on problem solution where I am collecting social feeds from twitter and Facebook for a product X . I am labeling these posts,comments or tweets using five labels ...
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47 views

Correct way to combine bigrams and unigrams in naive bayes classifier?

I'm writing naive bayes classifier for spam detection. I'd like it to recognize difference between "Paris Hilton" and "Hilton Paris", so I'm using bigrams in addition to unigrams. Currently I throw ...
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33 views

How to choose training sets for textual data?

I have textual data scraped from a classified ads site and I'm interested in classifying the posts into two categories: "X" and "not X". I'm stuck with coming up with a good training set and feature ...
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2answers
52 views

Evaluating unbiased errors on the Test set when cross validation scores are close

I trained a few different models, (Perceptron, Stochastic Gradient Descent and Naive Bayes), each with different parameters. I then scored their accuracy on a cross validation set. The scores on the ...
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1answer
52 views

Bayesian Classification evaluation

I am trying to implement Bayesian Classification on the data set as follows: "Problem: classify whether a given person is a male or a female based on the measured features. The features include ...
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2answers
162 views

Naive Bayes model [duplicate]

i am trying to implement the naive bayes algorithm in java. But i have a few confusions on the naive bayes model. What is the model of naive bayes? Is it a table ?how we can make prediction from the ...
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199 views

Predicting Naive Bayes model in R on a test data with a single record

I built a naivebayes model using the Housevotes84 data(discrete data) in mlbench package- model <- naiveBayes(Class~., data=HouseVotes84) I took one record ...