Your goal should be to get the best F1 measure possible without overfitting to

Your goal should be to get the best f1 measure

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of performance is easily attainable by selecting some of the features mentioned as suggestions above. Your goal should be to get the best F1 measure possible, without overfitting to the training data. The running time of your program must be reasonable, and must take limited computing resources into account. The Naïve Bayes method is fairly robust, and as such there are diminishing returns to using an excessive number of features. If your scores are particularly good, you will be asked to share a brief description of your preprocessing and feature selection with the class. 2. ( 10% ) Test your program – Re-arrange the data to experiment with different splits for training and test. Randomize you test and training sets. 3. ( 5% ) Code comments - Add the following comments to your code: A section at the top of the source file(s) with the following identifying information: Your Name CSCI 6350-001 Project #X Due: mm/dd/yy Below your name add comments in each file that gives an overview of the program or class. Place a one or two-line comment above each function that summarizes the workings of the function. 4. ( 25 % ) Analysis – Provide an answer (as commented code) to the following questions: 1. Explain what you did to preprocess the data, addressing what you tried that didn’t work, and state why you decided on the final approach you chose (include aspects in processing requirements: punctuation, text case, …) 2. Discuss the features you chose and why: How did you arrive at them? What did you try that did not work? 3. Use the output of the show_most_informative_features method of the classifier (the data is on a per run basis) and generalize why the top 5 features (over all 30 trials) worked as well as they did, given the input data.
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