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Question 1.9: What word in vocab_table was shortened the most by this stemming process? Assign most_shortened to the word. hint function len(str) will return the length of the input string str. You will do a loop over rows of the vocabulary to compute the length of each word

Question 1.9: What word in vocab_table was shortened the most by this stemming process? Assign most_shortened to the word. hint function len(str) will return the length of the input string str. You will do a loop over rows of the vocabulary to compute the length of each word

Computer Networking: A Top-Down Approach (7th Edition)
Computer Networking: A Top-Down Approach (7th Edition)
7th Edition
ISBN:9780133594140
Author:James Kurose, Keith Ross
Publisher:James Kurose, Keith Ross
Chapter1: Computer Networks And The Internet
Section: Chapter Questions
Problem R1RQ: What is the difference between a host and an end system? List several different types of end...
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Question 1.9: What word in vocab table was shortened the most by this stemming process? Assign most_shortened to the word. hint: function len(str) will return the length of the input string str. You will do a loop over rows of the vocabulary to compute the length of each word In [ ]: Splitting the dataset We're going to use our lyrics dataset for two purposes. First, we want to train various song genre classifiers. Second, we want to test the performance of our final classifier. Hence, we need two different datasets: training, and test. The purpose of a classifier is to generalize to unseen data that is similar to the training data Therefore, we must ensure that there are no songs that appear in two different sets We do so by splitting the dataset randomly. The dataset has already been permuted randomly, so it's easy to split We just take the top for training, and the last for test Question 1.10: Split the data with the ratio sex for training and 20% for testing In [ 1:
Transcribed Image Text:Question 1.9: What word in vocab table was shortened the most by this stemming process? Assign most_shortened to the word. hint: function len(str) will return the length of the input string str. You will do a loop over rows of the vocabulary to compute the length of each word In [ ]: Splitting the dataset We're going to use our lyrics dataset for two purposes. First, we want to train various song genre classifiers. Second, we want to test the performance of our final classifier. Hence, we need two different datasets: training, and test. The purpose of a classifier is to generalize to unseen data that is similar to the training data Therefore, we must ensure that there are no songs that appear in two different sets We do so by splitting the dataset randomly. The dataset has already been permuted randomly, so it's easy to split We just take the top for training, and the last for test Question 1.10: Split the data with the ratio sex for training and 20% for testing In [ 1:
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