Repeat this exercise with caps, the number of capital letters in a message, and misc, the number of characters in a message that aren\'t letters, digits, or spaces.

INSTRUCTIONS TO CANDIDATES

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import pandas as pd url = "https://raw.githubusercontent.com/mgreenbe/spam/main/spam.csv" df = pd.read_csv(url, encoding="latin-1") df.rename(columns={"v1": "label", "v2": "message"}, inplace=True) df.drop(columns=["Unnamed: 2", "Unnamed: 3", "Unnamed: 4"], inplace=True) df.head() Split the data into a training set (80% of the data) and a testing set (20% of the data). Compute the proportions of ham and spam in the training set.

Add a feature column length to the dataframe df containing the lengths of the messages, in characters. Classify the messages in the test set as ham/spam using a Gaussian (naïve) Bayes classifier trained on the length data. Record the associated testing error.

Repeat this exercise with caps, the number of capital letters in a message, and misc, the number of characters in a message that aren't letters, digits, or spaces.

(Since each of the classifiers in this part only considers a single feature, there is no distinction between Bayes and naïve Bayes.) Construct a Gaussian naïve Bayes classifier to the (length, caps, misc) data. Compare its performance to those considered in part (2).

Construct a Gaussian Bayes classifier by fitting a 3-dimensional Gaussian distribution to the (length, caps, misc) data. Feel free to use your above implementation of GaussianBayes here. Compare its performance to the naïve version considered in part (3). Comment.

Repeat part (2) with digits, the number of digits in a message. Comment. Construct a multinomial naïve Bayes classifier using frequency data as produced by CountVectorizer. Compare its performance to those considered above.

 

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