StratifiedKFold vs KFold in scikit-learn

StratifiedKFold vs KFold in scikit-learn

In machine learning, cross-validation is a crucial technique for evaluating the performance of a model. It involves dividing the dataset into multiple folds and using a different fold for testing while training on the remaining folds. This process is repeated for each fold, providing an estimate of the model’s generalization ability. Two popular cross-validation strategies in scikit-learn are KFold and StratifiedKFold. This article will delve into the differences between these two approaches and provide insights into when to use each one.

Understanding KFold

What is KFold?

KFold is a simple and widely used cross-validation technique. It randomly divides the dataset into k equal-sized folds. In each iteration, one fold is used as the test set, and the remaining k-1 folds are used for training. This process is repeated k times, with each fold serving as the test set once.

Example Code:

from sklearn.model_selection import KFold
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression

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

kf = KFold(n_splits=5, shuffle=True, random_state=42)
for train_index, test_index in kf.split(X):
  X_train, X_test = X[train_index], X[test_index]
  y_train, y_test = y[train_index], y[test_index]
  model = LogisticRegression()
  model.fit(X_train, y_train)
  # Evaluate the model on the test set

Understanding StratifiedKFold

What is StratifiedKFold?

StratifiedKFold is an extension of KFold that addresses the issue of class imbalance. When the dataset has a significant difference in the proportion of classes, KFold may not evenly distribute the classes across the folds, leading to biased results. StratifiedKFold ensures that the distribution of classes in each fold is proportional to the overall distribution of classes in the original dataset.

Example Code:

from sklearn.model_selection import StratifiedKFold
from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression

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

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]
  model = LogisticRegression()
  model.fit(X_train, y_train)
  # Evaluate the model on the test set

Key Differences Between KFold and StratifiedKFold

Summary Table:

When to Use Which?

• **Use KFold** when the dataset is balanced, and class distribution is not a major concern.
• **Use StratifiedKFold** when the dataset is imbalanced, and it’s essential to maintain a similar class distribution in each fold. This is particularly important in classification problems where misclassifying certain classes can have more severe consequences.

Conclusion

Both KFold and StratifiedKFold are valuable tools for cross-validation in machine learning. While KFold provides a general approach, StratifiedKFold is crucial when dealing with imbalanced datasets. Understanding the differences between these techniques allows you to choose the right approach for your specific problem, leading to more reliable and robust model evaluation.