What is an example of a concept drift?
Artificial Intelligence & Machine Learning
Concept drift is a phenomenon that occurs when the statistical properties of a dataset change over time, leading to degraded performance of machine learning models trained on the original data. It is a common problem in real-world applications where the data distribution can change rapidly due to changes in the environment, user behavior, or other factors. An example of concept drift can be observed in the field of credit scoring.
Credit scoring is the process of evaluating a borrower’s creditworthiness based on their credit history, income, and other factors.
Banks and other financial institutions use credit scoring models to determine whether to approve a loan application or not. Credit scoring models are typically trained on historical data that reflects the characteristics of borrowers and their repayment behavior.
However, the characteristics of borrowers and their behavior can change over time due to various factors such as economic conditions, changes in government policies, or changes in consumer behavior. For example, the emergence of new types of financial products, such as mobile payments or cryptocurrencies, may change the way consumers interact with financial services.
These changes in the environment can lead to concept drift. Where the statistical properties of the data used to train the credit scoring model no longer reflect the current reality. For instance, the creditworthiness of a borrower considered high risk a few years ago. May have improved significantly due to changes in their financial situation. Similarly, the risk associated with a certain type of loan may have changed due to changes in economic conditions or government policies.
Concept drift often leads degraded performance of credit scoring models, resulting in inaccurate loan decisions.
For example, if a model trains on data that is several years old. And does not reflect current conditions. It may approve loans now considered high risk. Leading to increased loan defaults and losses for the bank.
To address concept drift in credit scoring, financial institutions need to continuously monitor the performance of their models and update them with new data. They also need to develop techniques to identify and correct for concept drift, such as retraining the model with new data or adjusting the decision thresholds based on current conditions.
In conclusion, concept drift is a significant challenge in real-world applications of machine learning, including credit scoring. Changes in the environment can lead to degraded performance of models, resulting in inaccurate predictions and decisions. To address this challenge, organizations need to continuously monitor their models and adapt them to changing conditions, ensuring that they remain accurate and effective.
What is an example of a concept drift?
Machine Learning Concept Drift – What is it and Five Steps to Deal With it – Seldon
A Gentle Introduction to Concept Drift in Machine Learning – MachineLearningMastery.com
What is an example of a concept drift?
