Consider a dataset from an online store consisting of selected sets of five ebooks, which arerepresentative of the following categories: thriller, education and comedy. Using a documentcomparison technique, we can calculate the similarity of a new ebook b which is added to the store.The similarity represents the distance between the new ebook b and the five ebooks within each setfor the categories that are within the store.The table of similarity values of the new ebook b to the sample sets is shown below.Category Sample 1 Sample 2 Sample 3 Sample 4 Sample 5thriller0.430.110.030.230.34education0.670.120.360.120.72comedy0.080.310.80.10.53Consider a k-NN problem for k=3 with distance weighted average voting. What is the genre of thenew ebook b?Hint: The weight for each vote of a sample of a given category with distance d is calculated byw=1/d. For example, for Sample 1 in thriller, the weight is w=1/0.43 = 2.32. The distance weightedaverage voting calculates the weight of each vote based on the distance from the new data pointusing the previous formula for w. To make a prediction, you take the k nearest neighbours of the newsample, sum the votes indepently for each class and make the prediction based upon which class hasthe highest total vote.thrillercomedyit is not decidableeducation

As the data given above cannot clearly conclude the genre given. So.

Consider a dataset from an online store consisting of selected sets of five ebooks, which are representative of the following categories: thriller, education and comedy. Using a document comparison technique, we can calculate the similarity of a new ebook b which is added to the store. The similarity represents the distance between the new ebook b and the five ebooks within each set for the categories that are within the store. The table of similarity values of the new ebook b to the sample sets is shown below.

Consider a dataset from an online store consisting of selected sets of five ebooks, which are representative of the following categories: thriller, education and comedy. Using a document comparison technique, we can calculate the similarity of a new ebook b which is added to the store. The similarity represents the distance between the new ebook b and the five ebooks within each set for the categories that are within the store. The table of similarity values of the new ebook b to the sample sets is shown below.

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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TODO 12 Let's now split our input data X and labels y into a train and test set using the train_test_split() function (docs). Here we'll use the 80-20 split rule where we use 80% of the data for training and 20% for testing. Lastly, we'll seed our split using the random_state keyword argument which will make sure we create the same split every time we run the function. Use the train_test_split() function to get a train and test split. Store the output into X_train, X_test, y_train, and y_test. Pass the required arguments X and y. Further specify we want to use 20% of the training data by setting the test_size keyword argument. Lastly, pass the keyword argument random_state=42 to set the random seed so we get the same split every time we run this code. Print the shape for X_train. Print the shape for y_train. Print the shape for X_test. Print the shape for y_test. # TODO 12.1X_train, X_test, y_train, y_test = todo_check([ (X_train.shape == (413, 29), 'X_train does not have the…
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