Calculate the arithmetic mean of a dataset used in model feature analysis.
The arithmetic mean is the sum of all values divided by the count of values: mean = Σx / n. In ML pipelines, the mean is commonly used for feature normalization (subtracting the mean to center data), computing baseline metrics, and summarizing distributions of model outputs or errors.
mean = sum(x_i) / n
Very sensitive — since every value contributes proportionally to the sum, a single extreme outlier can shift the mean substantially; the median is a more robust alternative when outliers are a concern.
Feature means (and standard deviations) are typically computed on the training set and used to standardize inputs (zero mean, unit variance), which helps gradient-based optimizers converge faster and more reliably.
Yes — 'mean' and 'average' most commonly refer to the same arithmetic mean calculation; other averages like the median and mode measure central tendency differently.
Yes — this is a common way to compute a batch-average loss, confidence score, or metric across multiple predictions before logging or aggregating results.