Train-test split in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of a train-test split in machine learning?

It is used to divide data into two parts: one for training the model and one for testing its performance on new, unseen data.

beginner

What typical ratio is used for train-test split?

A common ratio is 70% to 80% of data for training and 20% to 30% for testing.

beginner

Why should the test set be separate from the training set?

To check if the model can generalize well to new data and avoid overfitting to the training data.

beginner

What Python library and function is commonly used for train-test splitting?

The scikit-learn library with the function train_test_split from sklearn.model_selection.

intermediate

How does random_state parameter affect train-test split?

It sets a seed for random shuffling so the split is reproducible and consistent across runs.

What is the main goal of splitting data into train and test sets?

A To evaluate model performance on unseen data

B To increase training data size

C To reduce the number of features

D To speed up model training

The test set helps check how well the model performs on new data it hasn't seen before.

Which of these is a common train-test split ratio?

A 50% train, 50% test

B 90% train, 10% test

C 70% train, 30% test

D 10% train, 90% test

70% train and 30% test is a typical split to balance training and evaluation.

What does the random_state parameter do in train_test_split?

A Sets the size of the test set

B Controls random shuffling for reproducibility

C Specifies the model type

D Normalizes the data

random_state ensures the split is the same every time you run the code.

Why should the test set not be used during training?

A To avoid overfitting and get an unbiased evaluation

B It helps tune hyperparameters

C It contains only noise

D It is smaller than the train set

Using test data in training can cause the model to memorize it, giving a false sense of accuracy.

Which Python function is used to split data into train and test sets?

A split_data()

B data_split()

C train_test()

D train_test_split()

train_test_split() from sklearn.model_selection is the standard function.

Explain why train-test split is important in machine learning.

Think about how you test a recipe by trying it on new guests, not just the cook.

Describe how you would perform a train-test split using Python.

Remember the function name and its main parameters.

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