Description: Output data normalization is a crucial process in data preprocessing that involves scaling the results generated by a model to ensure they are consistent and comparable across different models. This process is essential in the fields of machine learning and data mining, where models can produce outputs in varying ranges or units. Normalization allows output data to be adjusted to a specific range, such as [0, 1] or [-1, 1], thereby facilitating the comparison and interpretation of results. By normalizing output data, discrepancies that may arise due to variations in model scales are minimized, which in turn enhances the accuracy and effectiveness of decisions based on that data. This process not only helps maintain data integrity but also optimizes the performance of various algorithms, as many of them are sensitive to the scale of the data. In summary, output data normalization is a fundamental technique that ensures results are coherent and useful for subsequent analysis.
