Types of Data Drift
Last updated September 6, 2024
Data drift is a common challenge in machine learning, occurring when the distribution of your input data changes over time. This can negatively impact your model's performance as it may no longer accurately predict on new data. Understanding the different types of data drift is crucial for effective monitoring and model maintenance.
Types of Data Drift
- Concept Drift: This happens when the underlying relationship between your input features and the target variable changes. Imagine a model predicting loan defaults, but the factors influencing defaults change over time (e.g., due to economic shifts). Your model may become less accurate as its assumptions no longer hold.
- Data Schema Drift: Changes in the structure of your data, like adding new features, removing features, or altering data types, fall under data schema drift. These changes can break your model if it's not designed to handle them.
- Data Distribution Drift: This refers to changes in the statistical distribution of your input features. For example, the average income of your customer base may shift, or the frequency of certain values in a categorical feature might change. This can affect your model's prediction accuracy.
- Data Value Drift: Sometimes, the actual values in your data may start drifting. This can be subtle changes in specific features or more significant shifts. It requires careful analysis to detect and understand its impact on your model.
- Data Volume Drift: Changes in the amount of incoming data can also be considered data drift. This can occur when you have drastically more or fewer data points than anticipated, which might affect your model's performance if it's not trained to handle such variations.
- Label Shift: In supervised learning, label shift happens when the distribution of your target variable changes. For example, if your model is predicting customer churn, but the overall churn rate changes significantly, your model might struggle to adapt.
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