Train-test split in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Why do we use a train-test split in machine learning?

Choose the best reason why splitting data into training and testing sets is important.

A To evaluate how well the model performs on unseen data.

B To increase the size of the training data for better learning.

C To reduce the number of features in the dataset.

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

💡 Hint

Think about why we want to check the model's ability beyond the data it learned from.

We split data to test if the model can generalize to new, unseen data, not just memorize the training data.

❓ Predict Output

intermediate

Output of train-test split sizes

What will be the output sizes of training and testing sets after this code runs?

ML Python

from sklearn.model_selection import train_test_split
X = list(range(100))
y = [x * 2 for x in X]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42)
print(len(X_train), len(X_test))

A 75 25

B 25 75

C 70 30

D 80 20

💡 Hint

Test size is 0.25, so test set should be 25% of 100.

With test_size=0.25, 25% of 100 is 25 for testing, leaving 75 for training.

❓ Hyperparameter

advanced

Choosing the test_size parameter

Which test_size value is best if you want to maximize training data but still have a reliable test set?

A 0.05

B 0.2

C 0.5

D 0.8

💡 Hint

Think about a balance between enough training data and enough test data.

A test_size of 0.2 means 20% test data and 80% training data, a common good balance.

🔧 Debug

advanced

Identify the error in this train-test split code

What error will this code raise?

ML Python

from sklearn.model_selection import train_test_split
X = [1, 2, 3]
y = [4, 5]
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33)

A No error, code runs successfully

B TypeError: test_size must be an integer or float

C SyntaxError: invalid syntax

D ValueError: Found input variables with inconsistent numbers of samples

💡 Hint

Check if X and y have the same length.

X and y must have the same number of samples; here they differ, causing a ValueError.

❓ Model Choice

expert

Best practice for train-test split with imbalanced classes

You have a classification dataset with very imbalanced classes. Which train-test split approach is best to keep class proportions consistent?

A Split data manually by slicing the dataset in order.

B Randomly split data without stratify to avoid bias.

C Use train_test_split with stratify parameter set to the target labels.

D Use only training data and skip testing to avoid imbalance issues.

💡 Hint

Think about how to keep class distribution similar in train and test sets.

Using stratify ensures the train and test sets have the same class proportions, important for imbalanced data.

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