Understanding Train-test-split Function

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Engineering, Information Technology (IT), Architecture, Social Studies
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University
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Hard
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7 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is it important to remove irrelevant columns, such as the name column, when preparing data for machine learning?
To increase the number of features
To reduce the size of the dataset
To improve the model's ability to find patterns
To make the dataset more visually appealing
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What are the input and output variables referred to in coding terms?
A and B
X and Y
Input and Output
M and N
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of splitting data into training and testing sets?
To increase the size of the dataset
To test the model's accuracy
To make the dataset more complex
To simplify the data preparation process
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a common ratio for splitting data into training and testing sets?
90:10
50:50
60:40
70:30
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does the train_test_split function return?
Two arrays
Three arrays
Four arrays
Five arrays
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which parameter is NOT required when using the train_test_split function?
Output Y
Model type
Input X
Percentage for splitting
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main advantage of using the train_test_split function?
It improves model accuracy
It reduces the number of features
It increases the dataset size
It automates the data splitting process
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