Q1 Classification Write a function classify to conduct a classification experiements as follows: Take the training and testing file name strings as inputs, e.g. classify(training_file, testing_file). IClassify text samples in training file using linear support vector machine as follows: a. First apply grid search with 6-fold cross validation to find the best values for parameters min_df, stop_words, and C (penality parameter of SVM) that are used the modeling pipeline. Use f1-macro as the scoring metric to select the best parameter values. Potential values for these parameters are: min_df’ : [1,2,5] stop_words’ : [None,”english”] C: [0.5,1,5] b. Using the best parameter values, train a linear support vector machine classifier with all samples in news_train.csv Test the linear support vector classifier created in Step 2.b using the testing file. Compare f1-macro score you obtain from the test dataset with the f1-macro of the best model from grid search, and comment if the model is overfitted or not. Save your comment into a pdf fileYour function “classify” t has no return. However, when this function is called, the best parameter values from grid search is printed and the testing precision, recall, and f1 score from Step 3 is printed. Q2. How many samples are enough? Show the impact of sample size on classifier performance Write a function “impact_of_sample_size” as follows: Take the full file name path strings for training and test datasets as inputs, e.g. impact_of_sample_size(train_file, test_file). Starting with 300 samples from the training file, in each round you build a classifier with 300 more samples. i.e. in round 1, you use samples from 0:300, and in round 2, you use samples from 0:600, …, until you use all samples. In each round, do the following: create tf-idf matrix using TfidfVectorizer with stop words removedtrain a classifier using multinomial Naive Bayes modeltrain a classifier using linear support vector machine modelfor each classifier, test its performance using the testing file and collect the following metrics: macro precision, macro recall. Note, make sure you use the same model parameters for all iterations. Draw a line chart (two lines, one for each classifier) show the relationship between sample size and precision. Similarly, plot another line chart to show the relationship between sample size and recall Write your analysis on the following: How sample size affects each classifier’s performance? How many samples do you think would be needed for each model for good performance? How is performance of SVM classifier compared with Naïve Bayes classifier, as the sample size increases? There is no return for this function, but the charts should be plotted.Q3. Sentiment Classification You’ll need amazon_review_500.csv for this assignment. This csv file has two columns as follows. The label column provides polarity sentiment, either positive or negative Write a function detect_sentiment() as follows: Take the filename string as an input Create a Multinomial Naive Bayes classifier with 5-fold cross validation as a benchmarking model. The average testing f1 macro is about 70%. Try your best to improve average testing f1 macro by 5% out of 5-fold cross validation. You can use any approach. Some possible directions: Use different classification models, e.g. SVM Tune model parameters Add additional features, e.g. word POS or sentiment, phrases, negation words etc. Combine more than one models (i.e. ensemble). … This function has no return. Print out benchmarking performance and the improved performance when it’s called. Write a paragraph to describe your approach and explain why your approach would work. Note, This question has no standard answer. Your objective is to improve the average testing f1 macro of 5-fold cross valuation by 5% by whateve means! If you use additional resources, e.g. sentiment lexicon file, you need to submit these additional resource files
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