What preprocessing method is typically applied to numeric data in AutoML?

Scaling and normalization are essential preprocessing steps applied to numeric data, especially in automated machine learning (AutoML) contexts. These methods modify the range of feature values which helps in improving the performance of machine learning algorithms.

Scaling involves rescaling the features so that they conform to a certain range, such as [0, 1] or [-1, 1]. This is particularly helpful for algorithms that rely on distance measurements, because features with larger ranges can disproportionately influence the results.

Normalization, or z-score standardization, transforms data into a distribution with a mean of zero and a standard deviation of one. This standardization is beneficial in scenarios where the algorithms assume data is normally distributed.

By employing these techniques, models can converge faster during training and yield better results, making them fundamental in AutoML environments where model training needs to be efficient and effective. Other methods like feature extraction, data aggregation, and data augmentation serve different purposes and do not directly address the challenges with the scale and distribution of numeric data in the same way.