👉 Cleanup math, also known as normalization or scaling, is a crucial preprocessing step in machine learning that ensures data features are on a similar scale or distribution, which helps improve the performance and stability of algorithms. It involves transforming raw data values to a standard range, typically between 0 and 1, or to have a mean of zero and a standard deviation of one (standardization), or to follow a specific distribution like the Gaussian (normal) distribution (Z-score normalization). This process mitigates the impact of features with larger scales on model training, prevents features with higher magnitudes from dominating others, and can accelerate convergence during optimization. By standardizing data, cleanup math enhances the interpretability and efficiency of machine learning models, making them more robust to outliers and variations in input data.
