Data Normalization
Data normalization is the process of scaling numeric input values to a standard range, typically between 0 and 1 or -1 and 1. This prevents features with larger numerical ranges from disproportionately influencing a model's learning process. Common methods include Min-Max scaling and Z-score standardization, which transform data based on its minimum-maximum values or mean and standard deviation respectively, ensuring all features contribute more equally during training.
It's like making sure all ingredients in a recipe are measured in the same unit, so one ingredient doesn't overpower the others just because its quantity was given in a larger unit.
Proper data scaling can significantly improve the performance and convergence speed of many machine learning algorithms, especially those sensitive to feature magnitudes like neural networks and support vector machines.
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