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Underfitting refers to a situation where a machine learning model is unable to capture the underlying pattern and complexity of the data. It often occurs when the model is too simple or lacks sufficient training. As a result, the model performs poorly not only on the training data but also on new, unseen data. Underfitting can be identified when there is a high bias and low variance in the model's predictions. To address underfitting, one can try increasing model complexity, adding more features, or gathering more training data. Regularization techniques like L1 or L2 regularization can also help prevent underfitting by penalizing overly complex models and encouraging simplicity. Overall, underfitting is an important concept in machine learning as it highlights the need for finding an optimal balance between simplicity and capturing the true underlying patterns of the data.

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