Terahertz Engineering
Permutation importance is a technique used to evaluate the impact of individual features on the performance of a machine learning model by measuring the increase in the model's prediction error after permuting the feature values. This method provides insights into which features contribute most to the model's accuracy, helping to understand their significance in data analysis, particularly in complex datasets like those generated in terahertz measurements.
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