The process of scaling numerical data to a common range, typically between 0 and 1, to allow for fair comparison.
Provalo con l'IANormalization adjusts values measured on different scales to a notionally common scale. This is essential in data analysis and machine learning to ensure that variables with large ranges do not disproportionately influence the results.
(x - MIN(range)) / (MAX(range) - MIN(range))If you have a dataset with 'Age' (0-100) and 'Income' (0-100,000), normalizing both to a 0-1 scale prevents Income from dominating the calculation.
It prevents variables with larger absolute values from skewing your analysis or model performance.