method above vs stratify=y parameter in train_test_split

Often times, For imbalanced data, we can use stratify=y to maintain the same class distribution across training and test sets.

from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)

The stratify=y parameter in train_test_split helps to handle imbalanced data by ensuring that the class distribution in the training and testing sets matches the distribution in the original dataset. Here’s how it compares to other methods for handling imbalance:

How stratify=y Works

• Purpose: The stratify=y option in train_test_split ensures that both the training and testing sets have the same proportion of classes as the original dataset.
• Example: If the original dataset has 90% of class A and 10% of class B, stratify=y will ensure both the training and testing sets maintain that same 90:10 ratio.
• Benefit: Prevents a scenario where one of the splits (training or testing) has a much lower representation of the minority class, which can lead to biased training or unreliable evaluation results.

How stratify=y Compares to Other Methods

1. Complementary to Other Techniques:

• stratify=y does not actually balance the classes; it only preserves the original imbalance ratio across splits.
• Other methods like Oversampling, SMOTE, and Class Weights directly address the imbalance by increasing the representation of the minority class or adjusting the model’s focus on it.
• stratify=y can be used in combination with these methods to ensure consistency in class distribution across training and testing sets, making them more effective and reliable.

2. Impact on Model Performance:

• stratify=y does not alter the data (e.g., it does not add new samples or remove any); it only helps with consistent evaluation by ensuring the imbalance is present in both training and testing sets.
• In contrast, Oversampling, SMOTE, and Class Weights aim to directly counteract the imbalance effect by helping the model to learn from or give more attention to the minority class.

3. Comparison to Undersampling:

• Unlike undersampling, which removes majority class samples, stratify=y keeps all samples and does not reduce the dataset size. It only distributes them proportionally across training and testing.
• Undersampling actively balances the dataset (often to a 50:50 ratio), while stratify=y preserves the original imbalance.

Summary

In summary, stratify=y is primarily a consistency measure for data splits and is best used in conjunction with other imbalance-handling techniques. It doesn’t address class imbalance by itself but ensures that both training and testing sets reflect the original class distribution, allowing other techniques to work more effectively.