Using pipelines to avoid data leaks

One of the most common issues when working with machine learning models, particularly with time series data, is the potential for data leakage. Data leakage occurs when information from outside the training dataset is inadvertently used to train the model, often leading to over-optimistic performance metrics. Pipelines can play a vital role in mitigating this risk by ensuring that the same preprocessing steps applied during training are also applied consistently during inference.

For example, when working with time series data, applying transformations such as scaling or encoding to the entire dataset before splitting into training and testing sets can cause future information to leak into the training process. By using a pipeline, you can avoid this issue by ensuring transformations are applied only to the training data during training and then to the test data during inference.