Why is it important to split data into training and test sets in machine learning?
Deep Learning - Crash Course 2023 - Train Test Split

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Computers
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9th - 10th Grade
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Hard
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7 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
To make the data more complex
To increase the speed of data processing
To ensure the model is tested on unseen data
To reduce the size of the dataset
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the default ratio for splitting data into training and test sets using scikit-learn?
50-50
80-20
60-40
75-25
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In deep learning, what is the preferred ratio for splitting data into training and test sets?
95-5
90-10
80-20
70-30
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What parameter is used to specify the proportion of data to be used as test data in scikit-learn?
split_ratio
data_fraction
test_size
train_size
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Why is stratification important when splitting data?
To ensure equal distribution of classes in both sets
To increase the size of the training set
To make the test set more challenging
To reduce the complexity of the model
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the purpose of setting a 'random_state' in data splitting?
To randomize the data completely
To ensure reproducibility of results
To increase the randomness of the split
To decrease the size of the dataset
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which parameter helps in ensuring that the data split is reproducible?
shuffle
test_size
random_state
stratify
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