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

Question

Can you tell me 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 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:

• 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.

• Prevents Data Imbalance Issues:

  • Especially useful for imbalanced datasets where one class has significantly fewer instances than others.

• Applicable to Multi-Class and Binary Classification:

  • Works well for datasets with multiple classes (e.g., Iris dataset) and binary labels.

• 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.