Complete the TODO by finishing the accuracy() function. 1. Compute the total number of correct predictions by taking the np.sum() of the boolean array produced when comparing y_hat and y using == syntax. Store the output into total_correct. 1. Refer section on accuracy in the notes if you are confused as it shows you how to do this exactly! 2. Get the total number of data samples using y (or y_hat). Store the output into total_samples. 3. Return the accuracy score by dividing the total number of correctly classified data samples by the total number of data samples.
Complete the TODO by finishing the accuracy() function. 1. Compute the total number of correct predictions by taking the np.sum() of the boolean array produced when comparing y_hat and y using == syntax. Store the output into total_correct. 1. Refer section on accuracy in the notes if you are confused as it shows you how to do this exactly! 2. Get the total number of data samples using y (or y_hat). Store the output into total_samples. 3. Return the accuracy score by dividing the total number of correctly classified data samples by the total number of data samples.
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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Question
def accuracy(y, y_hat):
# TODO 8.1
total_correct =
# TODO 8.2
total_samples =
print(f"Accuracy ratio: {total_correct}/{total_samples}")
# TODO 8.3
return
def TEST_accuracy():
dummy_y = np.ones([100, 1])
dummy_y_hat = np.ones([100, 1])
dummy_y_hat[90:] = -1
dummy_acc = accuracy(dummy_y_hat, dummy_y)
print(f"Accuracy is: {dummy_acc}")
todo_check([
(dummy_acc == .9,"Incorrect accuracy value")
])
TEST_accuracy()
garbage_collect(['TEST_accuracy'])
Transcribed Image Text:Complete the TODO by finishing the accuracy() function.
1. Compute the total number of correct predictions by taking the np.sum() of the boolean array produced when comparing y_hat and y
using == syntax. Store the output into total_correct.
1. Refer section on accuracy in the notes if you are confused as it shows you how to do this exactly!
2. Get the total number of data samples using y (or y_hat). Store the output into total_samples.
3. Return the accuracy score by dividing the total number of correctly classified data samples by the total number of data samples.
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