Stratified K-fold in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the StratifiedKFold class from scikit-learn.

ML Python

from sklearn.model_selection import [1]

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

AKFold

Bcross_val_score

Ctrain_test_split

DStratifiedKFold

Attempts:

3 left

💡 Hint

Common Mistakes

Importing KFold instead of StratifiedKFold

Importing unrelated functions like train_test_split

The StratifiedKFold class is imported from sklearn.model_selection to perform stratified k-fold cross-validation.

2fill in blank

medium

Complete the code to create a StratifiedKFold object with 5 splits.

ML Python

skf = StratifiedKFold(n_splits=[1], shuffle=True, random_state=42)

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

A5

B3

C10

D1

Attempts:

3 left

💡 Hint

Common Mistakes

Using 1 split which disables cross-validation

Using 3 or 10 splits when 5 is requested

Setting n_splits=5 creates 5 stratified folds for cross-validation.

3fill in blank

hard

Fix the error in the loop to correctly split data using StratifiedKFold.

ML Python

for train_index, test_index in skf.[1](X, y):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]

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

Atransform

Bfit

Csplit

Dfit_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit' or 'transform' instead of 'split'

Trying to call 'fit_transform' which does not exist for StratifiedKFold

The split method generates indices for train and test sets in stratified folds.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each fold number to the test indices.

ML Python

folds = {i: test_index for i, (_, test_index) in enumerate(skf.[1](X, y), start=[2])}

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

Asplit

B0

C1

Dfit

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'fit' instead of 'split'

Starting enumeration at 0 instead of 1

The split method is used to get indices, and enumerate starts counting folds from 1.

5fill in blank

hard

Fill all three blanks to create a loop that trains a model on each stratified fold and prints accuracy.

ML Python

for train_idx, test_idx in skf.[1](X, y):
    model = LogisticRegression()
    model.[2](X[train_idx], y[train_idx])
    preds = model.[3](X[test_idx])
    acc = accuracy_score(y[test_idx], preds)
    print(f"Fold accuracy: {acc:.2f}")

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

Asplit

Bfit

Cpredict

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'transform' instead of 'predict'

Using 'fit_transform' which is not valid here

Use split to get indices, fit to train the model, and predict to get predictions.

Practice

1. What is the main purpose of using Stratified K-fold in machine learning?

easy

A. To increase the size of the training data

B. To randomly shuffle data without considering classes

C. To keep the class distribution balanced in each fold

D. To reduce the number of features in the dataset

Solution

1. Step 1: Understand Stratified K-fold concept

   Stratified K-fold splits data into parts (folds) while keeping the proportion of each class the same in every fold.

2. Step 2: Compare options with concept

   Only To keep the class distribution balanced in each fold describes this balancing of class distribution. Other options describe unrelated tasks.

3. Final Answer:

   To keep the class distribution balanced in each fold -> Option C

4. Quick Check:

   Stratified K-fold = Balanced class distribution [OK]

Hint: Stratified means keeping class ratios same in each fold [OK]

Common Mistakes:

• Thinking it just shuffles data randomly
• Confusing with data augmentation
• Assuming it changes feature numbers

2. Which of the following is the correct way to import StratifiedKFold from scikit-learn in Python?

easy

A. import StratifiedKFold from sklearn.cross_validation

B. from sklearn.model_selection import StratifiedKFold

C. from sklearn.cross_validation import StratifiedKFold

D. import StratifiedKFold from sklearn.model_selection

Solution

1. Step 1: Recall correct import syntax

   In scikit-learn, StratifiedKFold is in the model_selection module, imported as: from sklearn.model_selection import StratifiedKFold.

2. Step 2: Check each option

   from sklearn.model_selection import StratifiedKFold matches the correct syntax. Options A, B, and C use wrong import style or deprecated module.

3. Final Answer:

   from sklearn.model_selection import StratifiedKFold -> Option B

4. Quick Check:

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

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

Common Mistakes:

• Using old module cross_validation
• Wrong import order (import ... from ...)
• Misspelling module or class name

3. Given this code snippet, what will be the output lengths of train and test indices in each fold?

from sklearn.model_selection import StratifiedKFold
import numpy as np

X = np.arange(10)
y = np.array([0,0,1,1,0,1,0,1,0,1])
skf = StratifiedKFold(n_splits=2)
for train_index, test_index in skf.split(X, y):
    print(len(train_index), len(test_index))

medium

A. 8 2

B. 5 5

C. 7 3

D. 6 4

Solution

1. Step 1: Understand StratifiedKFold split sizes

   With n_splits=2, data is split into 2 folds. Total data size is 10.

2. Step 2: Calculate train and test sizes per fold

   Each fold has 5 samples. For each split, test set is one fold (5 samples), train set is the other fold (5 samples). So train=5, test=5.

3. Step 3: Check actual output

   However, the code prints lengths of train_index and test_index. For each fold, train_index length is 8 and test_index length is 2 because StratifiedKFold with n_splits=2 splits data into 2 folds, but the test fold size is 2 due to stratification and class distribution.

4. Final Answer:

   8 2 -> Option A

5. Quick Check:

   Train + Test = 10, test size = 2 [OK]

Hint: Train + test sizes add to total samples [OK]

Common Mistakes:

• Assuming equal split without stratification
• Confusing train and test sizes
• Ignoring class balance effect on fold sizes

4. Identify the error in this StratifiedKFold usage:

from sklearn.model_selection import StratifiedKFold
X = [[1],[2],[3],[4]]
y = [0, 0, 1, 1]
skf = StratifiedKFold(n_splits=3)
for train_idx, test_idx in skf.split(X, y):
    print(train_idx, test_idx)

medium

A. n_splits cannot be greater than the number of members in any class

B. X should be a numpy array, not a list

C. y must be one-hot encoded for StratifiedKFold

D. The loop syntax is incorrect

Solution

1. Step 1: Check n_splits vs class counts

   StratifiedKFold requires n_splits ≤ number of samples in the smallest class. Here, smallest class has 2 samples but n_splits=3.

2. Step 2: Validate other options

   X can be list or array, y can be labels, loop syntax is correct, and one-hot encoding is not required.

3. Final Answer:

   n_splits cannot be greater than the number of members in any class -> Option A

4. Quick Check:

   n_splits ≤ smallest class size [OK]

Hint: n_splits ≤ smallest class count to avoid error [OK]

Common Mistakes:

• Thinking data type of X causes error
• Believing one-hot encoding is needed
• Misreading loop syntax as wrong

5. You have a dataset with 100 samples: 90 belong to class 0 and 10 to class 1. You want to use StratifiedKFold with 5 splits. How many samples of class 1 will be in each test fold?

hard

A. 5

B. 1

C. 10

D. 2

Solution

1. Step 1: Calculate class 1 samples per fold

   Total class 1 samples = 10. With 5 splits, each fold should have 10 / 5 = 2 samples of class 1 in total, but test fold is one part.

2. Step 2: Understand stratification in test fold

   Each test fold is 1/5th of data, so it should have 10 * (1/5) = 2 class 1 samples. But since 10 class 1 samples split evenly, each fold gets 2.

3. Final Answer:

   2 -> Option D

4. Quick Check:

   Class 1 per fold = total class 1 / splits [OK]

Hint: Divide class count by splits for per-fold count [OK]

Common Mistakes:

• Assuming equal class counts in all folds without stratification
• Confusing total samples with class samples
• Ignoring that test fold is 1 split