The best way to split a dataset while preserving the target distribution in Scikit-learn is by using Stratified Sampling (StratifiedShuffleSplit or train_test_split with stratify), ensuring that the class proportions remain balanced in both training and testing sets.
Here is the code snippet given below:
In the above code we are using the following techniques:
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Uses train_test_split with stratify=y
- Ensures that the distribution of target classes in the original dataset is preserved in both training and testing sets.
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Prevents Data Imbalance Issues:
- Especially useful for imbalanced datasets where one class has significantly fewer instances than others.
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Applicable to Multi-Class and Binary Classification:
- Works well for datasets with multiple classes (e.g., Iris dataset) and binary labels.
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Random Seed for Reproducibility (random_state=42)
- Ensures that the same split can be reproduced across different runs.
Hence, using stratified sampling (train_test_split with stratify=y or StratifiedShuffleSplit) ensures that the training and testing sets maintain the same class distribution as the original dataset, preventing bias in model evaluation.
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