Central Tendency Measures

Central tendency measures quantify the "typical" values in a dataset, providing crucial insights for machine learning practitioners:

Mean: The arithmetic average, commonly used for evaluating model performance through metrics like mean squared error and mean absolute error. In model evaluation, a non-zero mean error suggests systematic bias where your model consistently overestimates or underestimates target values.

Median: The middle value when data is ranked, providing a robust measure less affected by outliers. This is particularly valuable when evaluating models on datasets with long-tailed error distributions or when comparing performance across different domains.

Mode: The most frequently occurring value, useful for understanding the most common predictions or errors in classification problems. The mode can reveal biases in your model's behavior toward particular categories.

In practice, comparing these measures across different slices of your data can reveal insights that would be missed by any single metric. For instance, a significant difference between mean and median prediction error often indicates that extreme outliers are skewing your evaluation metrics, suggesting the need for robust modeling approaches.