Description: Normalization of data characteristics is a fundamental process in data preprocessing that aims to adjust the features of a dataset to ensure they are on a similar scale. This adjustment is crucial because many machine learning algorithms and data analysis techniques are sensitive to the scale of the features. If the features have different ranges or units, they can disproportionately influence the model’s outcome, leading to misinterpretations or suboptimal performance. Normalization can involve techniques such as Min-Max scaling, which transforms values to a specific range, or z-score standardization, which adjusts the data to have a mean of zero and a standard deviation of one. This process not only improves model accuracy but also facilitates convergence during training, allowing algorithms to learn more efficiently. In summary, feature normalization is a critical step that ensures data comparability and enables machine learning models to function effectively.
