Underfitting occurs when a machine learning model is too simple to capture the underlying patterns in the data. This typically leads to poor performance on both the training set and the test set because the model fails to learn from the data effectively. In the context of learning algorithms, underfitting highlights the importance of balancing model complexity and data representation to achieve better predictive accuracy.
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