Description
Imagine a race car driver pushing their car to its limits. At some point, pushing harder doesn’t lead to faster lap times; instead, it increases the risk of a crash. Similarly, in the world of AI, training a model excessively can lead to overfitting, where it becomes too specialized in the training data and performs poorly on new, unseen data.
Early stopping acts like a skilled pit crew, monitoring your model’s performance and signaling when it’s time to hit the brakes. This prevents overfitting and ensures your model generalizes well to real-world scenarios.
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