What is the best way to split your dataset into training and testing sets in Scikit-learn while preserving the target distribution

Question

Can i know What is the best way to split your dataset into training and testing sets in Scikit-learn while preserving the target distribution?

Answer 1

The best way to split a dataset while preserving the target distribution in Scikit-learn is by using train_test_split with stratify=y or StratifiedShuffleSplit, ensuring that class proportions remain balanced in both training and testing sets.

Here  is the code snippet you can refer to:

In the above code, we are using the following code snippets:

• Preserves Class Distribution (stratify=y)

  • Ensures proportions of target classes remain the same in train and test sets.

• Prevents Imbalance in Training Data:

  • Especially useful for imbalanced datasets where one class is significantly underrepresented.

• Works for Both Multi-Class & Binary Classification:

  • Maintains balanced class representation, improving model generalization.

• Use StratifiedShuffleSplit for Fine-Tuned Splitting:

  • Ideal for multiple resampling iterations while keeping class proportions.

• Ensures Reproducibility (random_state=42)

  • Allows for consistent splits across different runs.

Hence, using train_test_split with stratify=y or StratifiedShuffleSplit ensures balanced class distributions in training and testing sets, leading to more reliable model evaluation