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python Programming Glossary: classifierImplementing Bag-of-Words Naive-Bayes classifier in NLTK http://stackoverflow.com/questions/10098533/implementing-bag-of-words-naive-bayes-classifier-in-nltk Bag of Words Naive Bayes classifier in NLTK I basically have the same question as this guy .. The.. guy .. The example in the NLTK book for the Naive Bayes classifier considers only whether a word occurs in a document as a feature.... seems to suggest this can't be done with the built in NLTK classifiers. Is that the case How can I do frequency bag of words NB classification..
use scikit-learn to classify into multiple categories http://stackoverflow.com/questions/10526579/use-scikit-learn-to-classify-into-multiple-categories of using more training examples or trying a different classifier Though note that the multi label classifier expects the target.. a different classifier Though note that the multi label classifier expects the target to be a list of tuples lists of labels. The.. it here and london too' target_names 'New York' 'London' classifier Pipeline 'vectorizer' CountVectorizer min_n 1 max_n 2 'tfidf'..
NLTK named entity recognition in dutch http://stackoverflow.com/questions/11293149/nltk-named-entity-recognition-in-dutch which does not work well on conll2002. Instead use a classifier based chunker like NaiveBayes so the full command might look.. a PER python train_chunker.py conll2002 fileids ned.train classifier NaiveBayes filename ~ nltk_data chunkers conll2002_ned_NaiveBayes.pickle..
How do I resolve 'NoneType' object has no attribute 'write' error with scikit-learn digits dataset? http://stackoverflow.com/questions/17139658/how-do-i-resolve-nonetype-object-has-no-attribute-write-error-with-scikit-le iris_y indices 10 # Create and fit a nearest neighbor classifier from sklearn.neighbors import KNeighborsClassifier knn KNeighborsClassifier..
Image segmentation based on edge pixel map http://stackoverflow.com/questions/18972932/image-segmentation-based-on-edge-pixel-map segmentation based on edge pixel map I have trained a classifier in Python for classifying pixels in an image of cells as edge..
Classifying Documents into Categories http://stackoverflow.com/questions/3113428/classifying-documents-into-categories on 5 categories used 8GB . Furthermore accuracy of the classifier seems to drop as I train on more categories 90 accuracy with.. 2 categories 81 with 5 61 with 10 . Should I just train a classifier on 5 categories at a time and run all 150k documents through.. at a time and run all 150k documents through the classifier to see if there are matches It seems like this would work except..
In Python small floats tending to zero http://stackoverflow.com/questions/3704570/in-python-small-floats-tending-to-zero you describe is a standard problem with the naive Bayes classifier. You can search for underflow with that to find the answer...
Suppressing output of module calling outside library http://stackoverflow.com/questions/4178614/suppressing-output-of-module-calling-outside-library learning library PyML . PyML uses libsvm to train the SVM classifier. The problem is that libsvm outputs some text to standard output...
Sentiment analysis for Twitter in Python http://stackoverflow.com/questions/573768/sentiment-analysis-for-twitter-in-python analyzing are VERY short they are tweets. So ideally this classifier is optimized for such short texts. BTW twitter does support.. as you're already familiar with implementing a Naive Bayes classifier. If not you may want to look into it and code one up to really..
Simple Digit Recognition OCR in OpenCV-Python http://stackoverflow.com/questions/9413216/simple-digit-recognition-ocr-in-opencv-python txt files we already saved earlier b create a instance of classifier we are using here it is KNearest c Then we use KNearest.train..
http://stackoverflow.com/questions/9860667/writing-robust-color-and-size-invariant-circle-detection-with-opencv-based-on what you're doing by trial and error. You're programming a classifier by learning from positive and negative examples. This is because..
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