Which practice is essential to avoid when preparing data to ensure integrity and reliability in machine learning?

Target leakage is a critical issue to avoid when preparing data for machine learning because it can lead to overly optimistic performance estimates and ultimately unreliable models. Target leakage occurs when information from outside the training dataset is used to create the model, particularly when that information is not available at the time of prediction in real-world scenarios. For example, if a model leverages data that would only be known after the outcome has already occurred, it can essentially "cheat," leading to inflated accuracy metrics during validation while failing in practical application.

In the context of machine learning, ensuring that your model generalizes well to unseen data is paramount. Target leakage can mask underlying issues in your model, resulting in poor performance when the model is exposed to new, real-world data. Proper data preparation that avoids leakage ensures that all features used for training are only those available at the time predictions are made, thereby maintaining the integrity and reliability of the machine learning process.

Other practices listed, such as data normalization, feature scaling, and data augmentation, are generally recommended techniques in data preparation that enhance the model’s performance. They help in transforming and enriching data rather than compromising its quality. Thus, avoiding target leakage is essential for creating robust and reliable machine learning models.