Cold start refers to the situation where a machine learning model is used for the first time with no prior training data. It could occur when a completely new user joins a platform or when a platform adds new products or services for which it has no prior user data. In this scenario, the model has no prior information to base its predictions on, and it must start from scratch to learn from the new data. Cold start can present a significant challenge for machine learning systems, as it requires the collection of new data and the design of new features to train the model. The accuracy and reliability of the model’s predictions are often lower in the early stages of cold start, but as it gathers more data and learns, it becomes more proficient and accurate in its predictions.
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