TODO 2 Print the shape for X_train. TODO 3 Print the shape for y_train. TODO 4 Print the shape for X_test. TODO 5 Print the shape for y_test. # TODO 2 # TODO 3 # TODO 4 # TODO 5
TODO 2 Print the shape for X_train. TODO 3 Print the shape for y_train. TODO 4 Print the shape for X_test. TODO 5 Print the shape for y_test. # TODO 2 # TODO 3 # TODO 4 # TODO 5
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
Related questions
Question
1. To obtain a train and test split, use the train test split() method. Save the output to the files X_train, X_test, y_train, and y_test.
- Pass the necessary arguments X and y.
- By specifying the test_size keyword parameter, we can further specify that we wish to use 20% of the training data.
- Finally, use the keyword option random_state=42 to establish the random seed, ensuring that we obtain the same split every time we run this code.
# TODO 1
X_train, X_test, y_train, y_test =
todo_check([
(X_train.shape == (413,29),'X_train does not have the correct shape (413, 29)'),
(X_test.shape == (104,29),'X_test does not have the correct shape (104, 29)'),
(y_train.shape == (413,),'y_train does not have the correct shape (413,)'),
(y_test.shape == (104,),'y_test does not have the correct shape (104,)'),
(np.all(np.isclose(X_train.values[-5:,-4], np.array([17.7,18.2,21.8,23.8,20.1]),rtol=.01)),'X_train does not contain the correct values! Make sure you used `X` when splitting!'),
(np.all(np.isclose(y_test.values[-5:], np.array([1.25561604,1.8531681,1.15373159,4.01259206,3.56558124]),rtol=.01)),'y_test does not have the correct values! Make sure you used `y` when splitting!')
])
TODO 2 Print the shape for X_train.
TODO 3 Print the shape for y_train.
TODO 4 Print the shape for X_test.
TODO 5 Print the shape for y_test.
# TODO 2
# TODO 3
# TODO 4
# TODO 5
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