Train-test split in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the function that splits data into training and testing sets.

ML Python

from sklearn.model_selection import [1]

Drag options to blanks, or click blank then click option'

Atrain_test_split

Btest_train_split

Csplit_train_test

Dtrain_test

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect function names like 'train_test' or 'test_train_split'.

Forgetting to import from sklearn.model_selection.

The correct function to split data into training and testing sets is 'train_test_split' from sklearn.model_selection.

2fill in blank

medium

Complete the code to split features X and labels y into training and testing sets with 30% test size.

ML Python

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=[1])

Drag options to blanks, or click blank then click option'

A3

B0.3

C30

D0.03

Attempts:

3 left

💡 Hint

Common Mistakes

Using integers like 30 instead of 0.3.

Using very small numbers like 0.03 which means 3%.

The test_size parameter expects a float between 0 and 1 representing the proportion of the dataset to include in the test split. 0.3 means 30%.

3fill in blank

hard

Fix the error in the code to correctly split data with a fixed random state for reproducibility.

ML Python

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=[1])

Drag options to blanks, or click blank then click option'

A42

BNone

C'random'

D0.25

Attempts:

3 left

💡 Hint

Common Mistakes

Using a string like 'random' instead of an integer.

Using None which means no fixed seed.

random_state should be an integer to ensure reproducible splits. 42 is a common choice.

4fill in blank

hard

Fill both blanks to split data with 20% test size and shuffle enabled.

ML Python

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=[1], shuffle=[2])

Drag options to blanks, or click blank then click option'

A0.2

BTrue

CFalse

D0.8

Attempts:

3 left

💡 Hint

Common Mistakes

Using 0.8 for test_size which means 80% test data.

Setting shuffle to False which disables shuffling.

test_size=0.2 means 20% test data, and shuffle=True means data is shuffled before splitting.

5fill in blank

hard

Fill all three blanks to split data with 25% test size, shuffle disabled, and random state 123.

ML Python

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=[1], shuffle=[2], random_state=[3])

Drag options to blanks, or click blank then click option'

ATrue

BFalse

C123

D0.25

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up True and False for shuffle.

Using a string instead of an integer for random_state.

Using 25 instead of 0.25 for test_size.

test_size=0.25 means 25% test data, shuffle=False disables shuffling, and random_state=123 fixes the split for reproducibility.

Practice

1. What is the main purpose of using a train-test split in machine learning?

easy

A. To check how well the model performs on new, unseen data

B. To increase the size of the training data

C. To reduce the number of features in the dataset

D. To speed up the training process by using less data

Solution

1. Step 1: Understand the purpose of train-test split

   Train-test split divides data into training and testing parts to evaluate model generalization.

2. Step 2: Identify the correct purpose

   The test set simulates new data to check model performance outside training data.

3. Final Answer:

   To check how well the model performs on new, unseen data -> Option A

4. Quick Check:

   Train-test split = Check model on new data [OK]

Hint: Train-test split tests model on unseen data [OK]

Common Mistakes:

• Thinking it increases training data size
• Confusing it with feature selection
• Believing it speeds up training by reducing data

2. Which of the following is the correct way to import the train_test_split function from scikit-learn?

easy

A. from sklearn import train_test_split

B. import train_test_split from sklearn

C. from sklearn.model_selection import train_test_split

D. import train_test_split from sklearn.model_selection

Solution

1. Step 1: Recall correct import syntax in Python

   Python uses 'from module import function' to import specific functions.

2. Step 2: Match with sklearn structure

   The function is in sklearn.model_selection, so correct import is from sklearn.model_selection import train_test_split.

3. Final Answer:

   from sklearn.model_selection import train_test_split -> Option C

4. Quick Check:

   Correct import = from sklearn.model_selection import train_test_split [OK]

Hint: Use 'from sklearn.model_selection import train_test_split' [OK]

Common Mistakes:

• Using wrong import order
• Trying to import directly from sklearn
• Using 'import' instead of 'from ... import'

3. What will be the output shape of X_train and X_test after this code?

from sklearn.model_selection import train_test_split
X = [[1], [2], [3], [4], [5]]
X_train, X_test = train_test_split(X, test_size=0.4, random_state=42)
print(len(X_train), len(X_test))

medium

A. 2 3

B. 5 0

C. 4 1

D. 3 2

Solution

1. Step 1: Calculate test set size

   Test size is 40% of 5 samples = 2 samples in test set.

2. Step 2: Calculate train set size

   Remaining samples go to train set: 5 - 2 = 3 samples.

3. Final Answer:

   3 2 -> Option D

4. Quick Check:

   Train size = 3, Test size = 2 [OK]

Hint: Multiply total by test_size for test count [OK]

Common Mistakes:

• Rounding test size incorrectly
• Swapping train and test sizes
• Ignoring random_state effect on split size

4. Identify the error in this code snippet:

from sklearn.model_selection import train_test_split
X = [1, 2, 3, 4, 5]
X_train, X_test = train_test_split(X, test=0.3)
print(X_train, X_test)

medium

A. The parameter name should be test_size, not test

B. train_test_split cannot split lists, only arrays

C. random_state parameter is missing

D. X should be a 2D list, not 1D

Solution

1. Step 1: Check parameter names for train_test_split

   The correct parameter to set test set size is 'test_size', not 'test'.

2. Step 2: Confirm other parameters

   train_test_split accepts lists and random_state is optional; 1D list is valid input.

3. Final Answer:

   The parameter name should be test_size, not test -> Option A

4. Quick Check:

   Correct parameter = test_size [OK]

Hint: Use 'test_size' parameter name exactly [OK]

Common Mistakes:

• Using 'test' instead of 'test_size'
• Thinking random_state is mandatory
• Believing input must be 2D

5. You have a dataset with 1000 samples. You want to split it so that the test set has exactly 250 samples. Which test_size value should you use in train_test_split?

hard

A. 0.3

B. 0.25

C. 0.2

D. 0.4

Solution

1. Step 1: Calculate test_size fraction

   Test samples desired = 250 out of 1000 total, so fraction = 250 / 1000 = 0.25.

2. Step 2: Match fraction to test_size parameter

   Set test_size=0.25 to get 250 samples in test set.

3. Final Answer:

   0.25 -> Option B

4. Quick Check:

   Test size fraction = 250/1000 = 0.25 [OK]

Hint: Divide test samples by total samples for test_size [OK]

Common Mistakes:

• Using absolute number instead of fraction
• Choosing wrong fraction like 0.2 or 0.3
• Confusing train_size with test_size