Description: Recalibration refers to the adjustment of data or models to improve accuracy. This process is fundamental in the field of data preprocessing, where the aim is to optimize the quality of information before analysis. Recalibration may involve modifying parameters in predictive models, correcting biases in data, or adapting the metrics used to evaluate model performance. By performing recalibrations, the goal is to make the results more representative of reality, which can lead to more informed and effective decisions. This process is especially relevant in areas such as artificial intelligence and machine learning, where the accuracy of models can significantly impact final outcomes. Recalibration not only improves the accuracy of models but can also help identify and mitigate overfitting issues, where a model becomes too tailored to training data and loses its ability to generalize. In summary, recalibration is an essential practice in data preprocessing that seeks to ensure that models and analyses are as accurate and useful as possible.
