naivebayes

When testing the naive classifier in matlab I get different results even though I trained and tested on the same sample data, I was wondering if my code is correct and if someone could help explain why this is? %% dimensionality reduction columns = 6 [U,S,V]=svds(fulldata,columns); %% randomly select dataset rows = 1000; columns = 6; %# pick random rows indX = randperm( size(fulldata,1) ); indX = indX(1:rows)'; %# pick random columns %indY = randperm( size(fulldata,2) ); indY = indY(1:columns); %# filter data data = U(indX,indY); %% apply normalization method to every cell data = zscore(data);

I try to do text classification naive bayes weka libarary in my java code, but i think the result of the classification is not correct, i don't know what's the problem. I use arff file for the input. this is my training data: @relation hamspam @attribute text string @attribute class {spam,ham} @data 'good',ham 'good',ham 'very good',ham 'bad',spam 'very bad',spam 'very bad, very bad',spam 'good good bad',ham this is my testing_data: @relation test @attribute text string @attribute class {spam,ham} @data 'good bad very bad',? 'good bad very bad',? 'good',? 'good very good',? 'bad',? 'very good'

I am attempting to run a supervised machine learning classifier known as Naive Bayes in the caret Package. My data is called LDA.scores, and has two categorical factors called "V4" and "G8", and 12 predictor variables. The code that I am using was adapted by a kind person on stack overflow from code supplied by myself (see link below).The code does work, however, only 9 predictors were used instead of the 12 predictors in the data-set. When I tried to train the Naive Bayes model with the total data set [2:13], the code failed. My next step was to systematically run the code with a subset of

I am trying to build a NaiveBayes classifier, loading the data from database as DataFrame which contains (label, text). Here's the sample of data (multinomial label): label| feature| +-----+--------------------+ | 1|combusting prepar...| | 1|adhesives for ind...| | 1| | | 1| salt for preserving| | 1|auxiliary fluids ...| I have used following transformation for tokenization, stopword, n-gram, and hashTF : val selectedData = df.select("label", "feature") // Tokenize RDD val tokenizer = new Tokenizer().setInputCol("feature").setOutputCol("words") val regexTokenizer = new RegexTokenizer()

When trying to fit Naive Bayes: training_data = sample; % target_class = K8; # train model nb = NaiveBayes.fit(training_data, target_class); # prediction y = nb.predict(cluster3); I get an error: ??? Error using ==> NaiveBayes.fit>gaussianFit at 535 The within-class variance in each feature of TRAINING must be positive. The within-class variance in feature 2 5 6 in class normal. are not positive. Error in ==> NaiveBayes.fit at 498 obj = gaussianFit(obj, training, gindex); Can anyone shed light on this and how to solve it? Note that I have read a similar post here but I am not sure what to do?

I'm trying a classification with python. I'm using Naive Bayes MultinomialNB classifier for the web pages (Retrieving data form web to text , later I classify this text: web classification). Now, I'm trying to apply PCA on this data, but python is giving some errors. My code for classification with Naive Bayes : from sklearn import PCA from sklearn import RandomizedPCA from sklearn.feature_extraction.text import CountVectorizer from sklearn.naive_bayes import MultinomialNB vectorizer = CountVectorizer() classifer = MultinomialNB(alpha=.01) x_train = vectorizer.fit_transform(temizdata)

I want to learn a Naive Bayes model for a problem where the class is boolean (takes on one of two values). Some of the features are boolean, but other features are categorical and can take on a small number of values (~5). If all my features were boolean then I would want to use sklearn.naive_bayes.BernoulliNB . It seems clear that sklearn.naive_bayes.MultinomialNB is not what I want. One solution is to split up my categorical features into boolean features. For instance, if a variable "X" takes on values "red", "green", "blue", I can have three variables: "X is red", "X is green", "X is blue"