Data Leakage is quickly becoming a buzzword in the AI community but do you know what it really means?
Look no further! We delve into exactly what Data Leakage is, example data set issues and some of the common causes of data leakage.
Finally, we’ll discuss how to prevent data leakage from happening in your own code.
What is Data Leakage in Machine Learning?
Data Leakage is a problem that arises when the data used for training does not accurately represent the data that will be used for inference.
In machine learning, this can occur through biased sampling, wrong data preprocessing, generating features from data used to calculate the target, training a model with features that are unavailable at inference time, or using features whose value changes over time.
How to Detect Data Leakage in Machine Learning
There are several ways to detect and prevent data leakage in machine learning. You can use hold-back validation strategies and split your dataset into two parts: a training set and a validation set. This is called cross-validation. You can also normalize your data correctly before cross-validation so you do not have any duplicates.
You can also use anomaly detection to detect unusual patterns in your data. This is especially useful when you have a large dataset that contains other types of anomalies, such as outliers and missing values.
Another option is Hold-Back Validation. Hold-Back Validation is a method of validation in which you hold back a portion of your data as a reserve. When you train your model with the rest of the data, you’ll be able to see how well it performs using only part of what it was trained on. This can help identify potential problems if the performance drops significantly when using just one set of data versus both sets together.
In summary, there are three main approaches to detecting data leakage in machine learning:
2. Anomaly Detection
3. Hold-Back Validation
Example Data Set of Data Leakage in Machine Learning
You need to make sure that the input layer has enough neurons to cover all possible inputs. If you don’t, your model might learn something about the data that isn’t true.
For example, suppose you’re trying to predict whether a person likes cats or dogs based on their age. The input layer should contain one neuron for each year between 0 and 100. But what happens if someone who doesn’t?
Likely, their data will be missing from the input layer. If this person likes dogs but is older than 100 years, then all of their neurons will be empty and your model won’t learn anything about them.
Why does Data Leakage happen?
Data Leakage is chiefly caused by a few factors:
Data Leakage Problem #1: Biased Sampling
When you are creating a dataset, it is important to have as many cases as possible. This can be hard when your data set is small, but it is still very important to include all types of people in your sample. If the sample only includes men and no women, then it will not accurately reflect the population as a whole.
This means that the data you collect may not be representative of the entire population. In some cases, this might mean that the data collected is biased toward one group of people.
For example, if you want to predict whether someone has diabetes based on their weight, you need to include both overweight and underweight people in your sample. However, if you only include obese people, you will get biased results since obesity is a risk factor for diabetes.
Data Leakage Problem #2. Wrong Data-Preprocessing
Wrong Data-Preprocessing is often the result of poorly designed questionnaires. If you are asking questions that can be interpreted differently by different people, then this will cause data leakage.
For example, if you ask “Do you have any pets?” and someone answers “Yes” but then clarifies that they don’t have any pets right now, this is a problem. The question should be more specific, such as “Do you have cats or dogs at the current moment?”
Data Leakage Problem #3. Generating Features From Data Used to Calculate the Target
When you calculate the target based on features from data used to calculate the target, then you are generating features. This is a problem because you will have more features than data points, which means that some of the information in those features might be irrelevant and can lead to overfitting problems.
Data Leakage Problem #4. Training A Model With Features That Are Unavailable at Inference Time
Features that are unavailable at inference time can lead to problems later on. This is because the model will not be able to make use of those features when making predictions or other decisions based on them. You need to remove any features that are unavailable at inference time before training the model so that it can be used effectively later on.
Data Leakage Problem #5. Using features whose value changes over time
Features whose value change over time can gunk up machine learning models because the model will not be able to keep up. You need to remove any features whose value changes over time before training the model so that it can be used effectively later on. Data Leakage Problem #6. Using features with missing values. Missing-values in your data can cause problems for machine learning models because the model will not be able to use those values when making predictions or other decisions based on them.
How to Prevent Data Leakage
The best ways to prevent data leakage are to ensure that the data is well-labeled and to have a robust model selection procedure. If you do not have enough labeled data for your problem, then it may be best to drop the idea of using matching learning altogether.
Instead, you can try a different type of learning that does not require labeled data to train. For example, you may want to consider using an ensemble model or deep learning architecture like a neural network.
Two other ways to prevent data leakage are to:
- Include a wide variety of features in your model
- Use regularization.
How to Fix Data Leakage Problem in Machine Learning
Ok, so what if you already have data leakage?
Well the best way to start fixing data leakage in ML is first to understand why the leakage happened. It is important to know whether it was a result of bad data or just an issue with how you trained your model. Once you have identified the problem, then you can begin fixing it by taking one of two different paths: either fix your data or fix your model.
Improving your data is a much more complicated task than fixing your model. There are many ways to clean data, and each one can have its own set of issues. For example, cleaning out noise may remove important information about the subject that you are trying to analyze. On the other hand, and removing too much noise can also make it difficult for your machine-learning algorithm to learn anything useful from the data.
Other ways to fix data leakage are to: split your dataset, normalize correctly before cross-validation and eliminate duplicates.
Data Leakage can occur for a variety of reasons but it can be kryptonite for machine learning. Like many problems, the best way to fix Data Leakage is to prevent it in the first place!