Stratified K-fold in ML Python

Introduction

Stratified K-fold helps us split data into parts while keeping the same balance of groups in each part. This way, our model learns fairly from all groups.

When you have different classes in your data and want each part to have the same class balance.

When you want to test your model fairly on all types of data.

When your data is small and you want to use all of it for training and testing.

When you want to avoid bias from uneven group distribution in splits.

Syntax

ML Python

from sklearn.model_selection import StratifiedKFold

skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)

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

X is your data features, y is the labels or groups you want to keep balanced.

shuffle=True randomizes data before splitting to get better mixes.

Examples

Splits data into 3 parts keeping class balance.

ML Python

skf = StratifiedKFold(n_splits=3)
for train_idx, test_idx in skf.split(X, y):
    print('Train:', train_idx, 'Test:', test_idx)

Shuffle data before splitting into 4 parts for randomness.

ML Python

skf = StratifiedKFold(n_splits=4, shuffle=True, random_state=1)
for train_idx, test_idx in skf.split(X, y):
    print(f'Train size: {len(train_idx)}, Test size: {len(test_idx)}')

Sample Model

This code splits the iris dataset into 3 parts with StratifiedKFold. It prints how many samples of each class are in train and test for each fold. This shows the class balance is kept.

ML Python

from sklearn.datasets import load_iris
from sklearn.model_selection import StratifiedKFold
import numpy as np

# Load iris data
data = load_iris()
X = data.data
y = data.target

# Create StratifiedKFold with 3 splits
skf = StratifiedKFold(n_splits=3, shuffle=True, random_state=0)

fold = 1
for train_index, test_index in skf.split(X, y):
    y_train, y_test = y[train_index], y[test_index]
    # Count classes in train and test
    unique_train, counts_train = np.unique(y_train, return_counts=True)
    unique_test, counts_test = np.unique(y_test, return_counts=True)
    print(f'Fold {fold}')
    print('Train class distribution:', dict(zip(unique_train, counts_train)))
    print('Test class distribution:', dict(zip(unique_test, counts_test)))
    fold += 1

Important Notes

StratifiedKFold is best for classification tasks where classes must be balanced.

If you use it for regression, it won't keep the target distribution balanced.

Always set random_state for reproducible splits.

Summary

Stratified K-fold splits data into parts keeping class balance in each part.

It helps models learn and test fairly on all classes.

Use it especially when classes are uneven or data is small.

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