Is your model not predicting as well as it used to? Identify and address model degradation

Is your model not predicting as well as it used to? Identify and address model degradation

You’ve designed and built a machine-learning model that predicts customer demand for one of your most popular products, at different prices. The model is extremely accurate––leveraging years of historical data on sales, customers, seasonality, and other variables. Therefore, the decision-makers in your organization are excited to start implementing it to enhance their targeting and improve their ROI. However, because they operate in fluid environments, machine-learning (ML) models require continuous monitoring to maintain their predictive power. This article will explain why models become less accurate over time, how you can track their performance to determine when they need to be updated, and strategies for addressing model degradation.  What is model degradation? Organizations use machine learning for artificial intelligence (AI) and to discern patterns within large amounts of data. Given a sufficient data set representing the actionable population (AKA “training data”), a computer learns to recognize such patterns and can then make predictions about previously-unseen data. Despite the name, machine learning doesn’t constantly learn....
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