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text classifier with weka: how to correctly train a classifier issue


text classifier with weka: how to correctly train a classifier issue

By : dcr_lena
Date : November 22 2020, 10:33 AM
With these it helps It seems like you changed the code from the website you referenced in some crucial points, but not in a good way. I'll try to draft what you're trying to do and what mistakes I've found.
What you (probably) wanted to do in extractFeature is
code :
Map<Integer,Double> featureMap = new TreeMap<>();
indices[i] = featureWords.size();
values[i] = (double) classValues.indexOf(inputInstance.stringValue(1));


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WEKA LibSVM Text Classifier

WEKA LibSVM Text Classifier


By : user4889964
Date : March 29 2020, 07:55 AM
With these it helps You might want to evaluate your approach to see if your 'on the right path'. Use cross validation to see if the models trained by your approach yield the performance you want.
how to access the python scikit learning code for Random Forest Classifier, Ada Boost Classifier, Extra Trees Classifier

how to access the python scikit learning code for Random Forest Classifier, Ada Boost Classifier, Extra Trees Classifier


By : Milan Knežević
Date : March 29 2020, 07:55 AM
will help you Check out this link for instructions on how to get the latest sources from git-hub. It is open source, so there is no issue getting the source. Like this:
code :
git clone git://github.com/scikit-learn/scikit-learn.git
What is the effect of using filtered classifier over normal classifier in weka

What is the effect of using filtered classifier over normal classifier in weka


By : user2705604
Date : March 29 2020, 07:55 AM
wish helps you When you using filter before N Fold cross validation you would be filtering every word appear in each instance despite being a test instance or train instance. At the moment Filter has no way to know if a instance is a test instance or a train instance. So if you are using StringtoWordVector with TFTransform or any similar operation, any word in test instances may affect the transform value. (Simply, if you are implementing bag of words then you would take test instance for the consideration too). This is not acceptable since the training parameters should not affected by the testing data. So instead you can do the Filtering on the run. That is FilteredClassifer.
For get an idea about how N Fold cross validation works, please refer to the Rushdi Shams's answer in following question. Please let me know if you understood it or not. Cheers..!!
Extract features from text file and train them to classifier

Extract features from text file and train them to classifier


By : Greg
Date : March 29 2020, 07:55 AM
will help you You need to transpose the feature matrix.
The reason of this is that all scikit-learn functions are expecting a matrix X as input where the rows are the subjects(samples) and the columns are the `features(variables)?.
code :
import numpy as np

features = np.array(features)
X = features.T

clf.fit(X,....)
Using WEKA classifier model in Java for classifying real time text

Using WEKA classifier model in Java for classifying real time text


By : Lukasz Doleczek
Date : March 29 2020, 07:55 AM
hope this fix your issue Although the previously suggested post is very nice, I believe that the one I produced some time ago better fits your needs, as it specifically deals with text, and it is generic regarding the classifier. Please check "A Simple Text Classifier in Java with WEKA".
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