👉 Imputation resescibility is a statistical concept that describes how much of a missing value in a dataset can be estimated from other available data. It's often used to estimate the missing values in a dataset, such as for regression analysis or forecasting models. In simple terms, imputation resescibility measures how well we can estimate the missing values in our dataset based on the information that is available. For example, if you have a dataset with two columns: "Age" and "Height", and
