Una distribución describe la frecuencia, el rango y la probabilidad de valores posibles de una característica, etiqueta, variable o salida de modelo. Comprender las distribuciones es esencial para el análisis de datos, la detección de drift, la evaluación de modelos y la evaluación de incertidumbre.
The frequency and range of different values for a given feature or label. A distribution captures how likely a particular value is. The following image shows histograms of two different distributions: - On the left, a power law distribution of wealth versus the number of people possessing that wealth. - On the right, a normal distribution of height versus the number of people possessing that height. !Two histograms. One histogram shows a power law distribution with wealth on the x-axis and number of people having that wealth on the y-axis. Most people have very little wealth, and a few people have a lot of wealth. The other histogram shows a normal distribution with height on the x-axis and number of people having that height on the y-axis. Most people are clustered somewhere near the mean. Understanding each feature and label's distribution can help you determine how to normalize values and detect outliers. The phrase out of distribution refers to a value that doesn't appear in the dataset or is very rare. For example, an image of the planet Saturn would be considered out of distribution for a dataset consisting of cat images.