Computational Biology
Mean Squared Error (MSE) is a metric used to measure the average squared difference between predicted values and actual values in a dataset. It quantifies how far off predictions are from actual outcomes, making it crucial for evaluating the performance of supervised learning algorithms, particularly in regression tasks. A lower MSE indicates better model performance, as it means the predictions are closer to the true values.
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