The Ultimate Guide To Data Leakage In Machine Learning

Dealt with a situation where the data acquisition is more than ideal? It barely sounds like a situation. Well, what if I say it is? Training your model with information that won’t be available when your model is out there in the wild is a problem that can lead to bloated lab accuracy, which will not translate out in the open. Such models can significantly harm your reputation among customers.

Well, don’t worry; NimbleBox is here to rescue you! Data Leakage and its associated problems can dramatically affect how customers perceive your model or product. Let us look deeply at these issues and what can be done to fix them.

What is data leakage in machine learning?

Data leakage in machine learning refers to including information in the training data that would not be available at the time of prediction. This can lead to overfitting and poor generalization because the model has been trained on data that would not be available at runtime.

For example, suppose you are building a machine learning model to predict which loans will default. If you include information in the training data about whether a loan has already defaulted, this would not be available at the time of prediction because the prediction is made before the loan defaults. The model would then use this "leaked" information to predict and would likely perform poorly on new, unseen data.

To avoid data leakage, it is essential to carefully consider what information should be included in the training data and ensure that it represents the conditions present at runtime.

What causes data leakage in machine learning?

There are several common causes of data leakage in machine learning:

Incorrect data splitting: If the data is not correctly split into training, validation, and test sets, it is possible for information from the test set to be included in the training data, leading to data leakage.

Incorrect data preprocessing: If data preprocessing steps are not adequately accounted for, information from the future can be included in the training data. For example, if you are building a model to predict a company's stock price and include the future stock price as a feature in the training data, this would be a form of data leakage.

Incorrect feature engineering: Similar to data preprocessing, if features are derived from data that would not be available at the time of prediction, this can lead to data leakage.

Human error: Data leakage can also occur due to human error, such as accidentally including data that should not be included in the training set.

Leaked information in the training data: If the training data contains information that would not be available at the time of prediction, this can also lead to data leakage.

Examples of data leakage machine learning

It might be challenging to get what we mean by Data Leakage just from the points influencing it, so let us look at some of the examples of this situation:

1. A model is trained to predict the likelihood of a customer churning (leaving the company) based on their past behavior. The model is trained on data that includes the customer's current status (whether they have churned or not). However, at the time of prediction, this information is not available, as the forecast is being made before the customer has churned. This is a form of data leakage because the model uses information that would not be available at the time of prediction.

2. A model is trained to predict the likelihood of a loan default based on the borrower's credit score and other financial information. The model is trained on data that includes whether the loan has already defaulted. However, at the time of prediction, this information is not available, as the forecast is being made before the loan has defaulted. This is a form of data leakage because the model uses information that would not be available at the time of prediction.

3. A model is trained to predict a company's stock price based on various financial and market data. The model is trained on data that includes the future stock price. However, at the time of prediction, this information is not available, as the forecast is being made before the future stock price has been determined. This is a form of data leakage because the model uses information that would not be available at the time of prediction.

What are some ways to address data leakage in machine learning?

Let us now look at the reason why you clicked on this article and see what can be done to avoid data leakage in your machine learning pipeline:

1. Holdout Set: One common approach to handling data leakage is to use a holdout set, a set of data that is set aside during training and only used for evaluation. This ensures that the model is only trained on the training data and not on any information from the test set.

2. K-fold Cross Validation: Another approach is to use K-fold cross-validation, which involves partitioning the data into K "folds" and training the model K times, each time using a different fold for testing and the remaining K-1 folds for training. This can help prevent information from the test set from "leaking" into the training set.

3. Time-based validation: This technique is mainly used for time series or other data types with a temporal component. The data is sorted by time before splitting into training and testing sets.

4. Create a separate validation set: Instead of using k-fold cross-validation, you can also create an entirely separate validation set, which is not used during the training process but only for evaluating the model.

5. Use a different dataset entirely: When working with real-world datasets, there are some unwanted variables or data that could potentially cause data leakage. In this case, it's a good idea to use a different dataset altogether.

It's important to note that data leakage can be challenging to detect and may only sometimes be noticeable. It's always a good idea to check your model's performance on a held-out test set to ensure that it generalizes well to new data.

Conclusion

In conclusion, data leakage is a common problem in machine learning that can lead to poor model performance and reduced trust in the results. To prevent data leakage, it is essential to carefully consider what information should be included in the training data and ensure that the data represents the conditions present at runtime.