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
0answers
6 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
30 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, ...
0
votes
0answers
30 views

What is the Bernoulli class conditional distribution?

What is the Bernoulli class conditional distribution? I am trying to implement a procedure for computing a naive Bayes classifier for binary features with a Bernoulli class conditional distribution. ...
0
votes
0answers
10 views

SMOTE sampling does massively worsens results of Naive Bayes compared to up or down sampling

I train Naive Bayes (NB) and and artificial neural network (ANN) an imbalanced multiclass problem. In order to deal with the imbalance I resample the data set. Using 10-fold cv, the kappa statistics ...
0
votes
0answers
20 views

How do I reduce number of features without rebuilding the model?

I'm pretty new to ML/NLP thus my question maybe naive. How do I reduce number of features without rebuilding the model for Naive Bayes Classification? I'm using MALLET to build the model to classify ...
1
vote
2answers
51 views

Classifying time-series similarity - what variable should I train on?

I have ~10,000 time series, each with 65 time points. I'm interested in classifying each pair of time series as "similar" or "not similar". Here's an example of two similar (left) and not similar time ...
0
votes
1answer
24 views

Naive Bayes error with caret

I want to predict a variable with Naive Bayes. I tried it with another one from the same dataset and it worked perfect but not with the desired. The variable to predict contains values like "OL","DL" ...
-1
votes
1answer
25 views

Big data set for Document Classification [closed]

I'm looking for big data set which is suitable to be used for document classification task. The data set which I'm looking for should composed of the frequency of the words which exist in each ...
1
vote
1answer
32 views

SVM classifier - can I average multiple models?

I'm performing SVM classification on a relatively large data set (~1M rows, 4 variables). I want to assign a classification score to each row, not evaluate input parameters, so following the top ...
1
vote
0answers
21 views

Is the Laplace/Lidstone smoothing parameter (talking about Multinomial/Bernoulli Naive Bayes) related to the particular structure of the dataset?

I'm working with Multinomial and Bernoulli Naive Bayes implementation of scikit-learn (python) for text classification. I'm using the 20_newsgroups dataset. From the scikit documentation we have: ...
0
votes
0answers
8 views

Naive bayes - parameters count

How many parameters do we have to estimate using naive bayes when the input features are conditionaly Independent and when they are not? Is there a formula that can fit - boolean, discrete and ...
0
votes
0answers
33 views

Why is the “training score” I get from the learning curve of Multinomial Naive Bayes so different from the training score of the Bernoulli version?

I'm comparing the learning curves of Bernoulli and Multinomial Naive Bayes using the 20_newsgroups dataset from scikit-learn for text-classification. I considered both the "training score" and the ...
0
votes
0answers
29 views

R - Plotting a ROC curve for a Naive Bayes classifier using ROCR. Not sure if I'm plotting it correctly

I have a Naive Bayes classifiers that I'm using to try to predict whether a game is going to win or lose based on historical data. The model has 25 variables in total, all of which are categorical ...
1
vote
0answers
13 views

Naïve Bayes with different distributions for each feature

I am looking at how naive Bayes works and I see that it goes over all the classes and finds the probability that maximizes: $\log(\operatorname{Pr}[Y=y]) + \sum_{i=1}^d ...
0
votes
0answers
10 views

Is a one class naive bayes possible?

I have a simple question - I think. I have recently read a paper: ...
0
votes
2answers
37 views

Predicting with cross validation

I want to predict labels via naive bayes and cross validation and measure the test accuracy. I do understand the principle of cross validation but not completely how to apply it. My question: Do I ...
2
votes
0answers
40 views

Combining multiple classifiers

I am trying to do a binary classification of text articles into {relevant, non-relevant}. The text articles have following features: [[article text, ...
0
votes
1answer
12 views

Using Clustering Coefficient to Improve Naive Bayesian Classifier

I am new at statistics and ML. Due to my lack of theoretical background I was wandering if does it make sense to combine NBC and CC. I am participating to the kaggle competition ...
0
votes
0answers
28 views

Is MAP and MLE the same if MAP uses uniform priors?

would MAP = maximum a posterior and MLE = maximum likelihood estimation be the same if the priors were uniform? since maximizing p(x|y) would be basically the same as p(x|y)c where c is some ...
0
votes
0answers
19 views

Is fitting hyperparameters to data in a Machine Learning model appropriate?

I have constructed a machine learning model (it is similar to Naive Bayes) within the Bayesian framework, and as such, have must select priors. In my brief exposure to Bayesian statistics, I was ...
2
votes
1answer
45 views

What is the correct spelling and capitalization of “Naive Bayes”?

I wonder which form(s) are correct amongst the following: Naive Bayes (example: Tom Mitchell's chapter on Naive Bayes) naive Bayes (example: the Wikipedia page on naive Bayes) I have also read ...
1
vote
0answers
25 views

Probability of correct classification with optimal Bayes when increasing number of features

Consider the optimal Bayes classifier applied on a problem with N features. Let its probability of correct classification be $$P_N(corr)$$ Assume that we add an extra feature (so now we have N + 1 ...
0
votes
0answers
32 views

Implementation of Naive Bayes Classifier in Excel?

I need help for implementation of Naive Bayes algorithm purely in Microsoft excel. I have 5 independent variable. I have already implemented in R but I would like to know is there any way to implement ...
3
votes
1answer
24 views

Document classification sample size

I'm working on a document binary classification problem where I have a decent sized corpus of about 30,000 documents (600-1000 words each). My approach is to select a sample of documents and manually ...
3
votes
1answer
54 views

How to combine probabilities of belonging to a category coming from different features?

Let us consider a problem of binary classification based on use of several nominal (categorical variables). For example, we would like to predict if a person has a car based on his/her gender, ...
0
votes
0answers
24 views

Accuracy decreased after feature selection

For my machine learning study, I tested different algorithms like SVM, SMO, Naive Bayes, Trees etc. All the algorithms resulted with low accuracy levels. In fact the highest accuracy I obtained was ...
0
votes
0answers
17 views

Logistic regression vs. PPV

I need to predict individuals' risk of an important binary outcome using a 10-item scale of risk factors. From a sample of 700 subjects I have fixed follow-up outcome frequencies for each total score. ...
0
votes
0answers
11 views

Multinomial Naive Bayes Failing for Identity Mapping

I am trying to find out why the MultinonmialNB classifier sklearn.naive_bayes fails when assigned to predict its own class with ...
1
vote
1answer
64 views

Is it wrong if I get training accuracy lower than test accuracy?

I have a dataset with 20000 instances in training, 2300 attributes. I did 10 fold CV and executed on a test set with 9000 instances with naive bayes and J48. The 10 fold CV accuracy is low compared to ...
0
votes
1answer
38 views

Naive Bayes for Spam detection

I am studying few examples of simple Naive Bayes for Spam detection. I had a question it, but I am unable to find it in any of the examples. I was wondering, what will happen if a word appears ...
0
votes
1answer
52 views

What does it mean “Disadvantage of Naive Bayes Classifier: strong feature independence”?

It is told that "the most important disadvantage of Naive Bayes is that it has strong feature independence assumptions". Can some please explain this more elaborately? Thank you in advance.
1
vote
1answer
38 views

How large of a percentage of my training set do I have to use to perform feature selection?

I have a data set that has 660,000 samples with 72 features and I'm trying to perform feature selection so that I can train a naive bayes classifier. The problem is that since the data set is so big, ...
0
votes
0answers
23 views

Confusion of bayesian spam filtering

From naive bayes spam filtering, we know that \begin{equation} p(S|W_1W_2) = \frac{p1p2}{p1p2 + (1-p1)(1-p2)} \end{equation} where $p_i$ means $p(S|W_i)$, the probability that the email is spam ...
2
votes
1answer
78 views

When is a Naive Bayes Model not Bayesian?

I've been using Bayesian inference for a while and as far as I could tell, Naive Bayes was "Bayesian" since it has a prior and a posterior and follows the Bayes rule. I just read a topic on ...
1
vote
1answer
23 views

Alternative Smoothing techniques for Naive Bayes?

I've always wondered why no one using smoothing techniques for Naive Bayes which implicitly test which conditional probabilities should actually be used and which ones shouldn't. The formula for ...
0
votes
0answers
23 views

Naive Bayes where Feature Space is LDA Output

I understand that the output for Latent Dirichlet Allocation is a distribution over K topics. Suppose I have a Dx(K+1) matrix, where rows are documents and columns are the topic distribution + one ...
0
votes
0answers
11 views

Calculate mutual information between the features and the class given the naïve Bayes assumption

I am reading a paper by Irina Rish(http://www.research.ibm.com/people/r/rish/papers/RC22230.pdf), and I would like to know what the notation I(C;(X1,X2)) i.e the mutual information between the ...
1
vote
0answers
23 views

Which is a better method to combine probability in Naive Bayes?

The Wikipedia article "Naive Bayes Spam Filtering" https://en.wikipedia.org/wiki/Naive_Bayes_spam_filtering#Mixed_methods) mentions two methods of combining probability that addresses underflow ...
0
votes
0answers
29 views

How do you use Naive Bayes to predict a class/label by hand?

Lets say I'm give this information about this table: Predictions are made on five testing points using this classifier. Let Noncontact represent the positive class label. The predicted positive class ...
0
votes
1answer
19 views

What's the best way to calculate Naive Bayesian Classifier by hand?

For example, lets suppose that if i use the data below to learn Naive Bayesian Classifier. Using this classifier, I would have to calculate p(NonContact, p(HardContact), and p(SoftContact) for the ...
1
vote
2answers
74 views

Comparing two Machine learning Models using ROC curves

I have developed an SVM-Model using x data. ROC curve was generated using 5-fold cross-validation. Now I want to compare my new ...
2
votes
1answer
76 views

How many observations do I need to use Naive Bayes?

My goal is to apply a Naive Bayes classifier to predict whether a user will like or dislike an item on a website based on a few attributes and his previous feedback. The problem is that I don’t have ...
0
votes
0answers
66 views

Supervised Binning with Naive Bayes

Context. I am working on a model to predict "churn". Subscription service where users pay a monthly fee to access the service. We would like to predict which accounts are likely to cancel or "churn". ...
0
votes
0answers
45 views

Increasing data leads to poorer performance

First, I'm new at Machine Learning, but I'm having to drink from the firehose while implementing. I have truck-loads of parsed timeseries data for thousands of subjects, from which I'm trying to ...
1
vote
1answer
134 views

Choosing a model for classification: decision tree or naive bayes

student My goal is to predict churn for a monthly subscription model business. The data set has a small number of dimensions: subscription id start date (can infer "months as a subscriber") price ...
0
votes
0answers
59 views

Machine learning model for prediction

I have collected labeled data from different users. These data are individualized (psychological status). I want to create prediction models for each user. How many instances (min, max) do I need to ...
1
vote
1answer
45 views

Can an SVM perform better than an Ideal Observer?

Under what circumstances is the above not true? It seems that in fields like vision science, the ideal observer is preferred against other classifiers when the underlying distributions are known for ...
0
votes
0answers
30 views

weighted bayes theorem

I am using a simple implementation of Bayes theorem to find the discrete probability distribution of proportion of wins. ...
0
votes
1answer
43 views

Quantifying the error from a Bayesian model

I have a Bayesian model that predicts the chance of some event occurring. This is basically a binary classification. To view the accuracy, I took the prediction output of my test data and binned it ...
2
votes
2answers
66 views

Why class stratified sampling is not compatible for naïve bayesian modeling, if sampling is used?

I read a book recently and it mentioned related to prior probability of naïve bayesian: "Since the probability of an outcome is calculated from the data set, it is important that the data set used ...