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Sobrequalificação, also known as overfitting, is a common problem in machine learning where a model learns the training data too well, including its noise and specific patterns, leading to poor performance on unseen data. Essentially, the model becomes too specialized to the training set and fails to generalize. This occurs when a model is too complex for the amount of data available, or when it is trained for too long.

Signs of overfitting include a very low error rate on the training data, but a significantly higher error rate on validation or test data. The model has effectively memorized the training examples rather than learning underlying relationships. Techniques to combat overfitting include using more data, simplifying the model, employing regularization methods (like L1 or L2 regularization), and using cross-validation to assess generalization performance. Early stopping during training is another strategy where training is halted when performance on a validation set begins to degrade, even if training set performance continues to improve. The goal is to find a model that strikes a balance between fitting the training data and generalizing to new, unseen data.