In many cases, ensuring the robustness of a model is critical for a good consistency and generalization of unseen data. Detecting influential individual data observations can be another crucial reason to avoid inaccurate results.

This process often involves assessing the variability of the model’s output and identifying potential bias, especially when dealing with small datasets. One powerful statistical tool to address these challenges is the Jackknife estimation method.

In this article, we’ll deep-dive into the concept of Jackknife estimation, walk through a practical example, and explore step-by-step how it works.

What is Jackknife Estimation?

As Bootstrapping, Jackknique estimation is a resampling statistical technique to estimate bias and variance of an estimator. It works by leaving out one observation at a time from a dataset, calculating the estimator on the remaining data, and then using the resulting estimates to compute the overall estimate. To illustrate the usage of this technique, we will explain later a common practical example about churn prediction.