Decoding tracebacks using machine learning

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I am trying to solve a problem where I have files which contain decoded- tracebacks( Stack call trace) whenever there is a Crash (in Linux world) and I have a unique ID to track the Crash occuring each time.

I want to build a classfier which will learn from the previous decoded-tracebacks and predict if there is an already existing ID for current traceback seen.

This is my first machine learning project . I used machine learning and did a trial using CountVectorizer and TF-IDF approach in python.

I want to know which features to consider for classification and suitable algorithm for text-classification to solve this problem.

data-science tf-idf traceback machine-learning python
1 Answers

great to hear that this is your first machine learning project! For my first NLP, i'm using the Amazon product reviewed to doing it. Do you try the Bag of words (BOW) model? And you can try N-gram too. And you can consider to use NaiveBayes Classifier and evaluate your classification. Then you will know which will give you the best algorithm to solve the problem.

Extra reading (if you like) : https://machinelearningmastery.com/encoder-decoder-models-text-summarization-keras/

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