Day 40 of #100dayswithmachinelearning Topic -- Multivariate Imputation by Chained Equations [ MICE ] A Thread 🧵
MICE stands for Multivariate Imputation By Chained Equations algorithm, a technique by which we can effortlessly impute missing values in a dataset by looking at data from other columns and trying to estimate the best prediction for each missing value.
Missing Completely at Random (MCAR) - Implies the missingness of a field is completely random, and that we probably cannot predict that value from any other value in the data.
Missing at Random (MAR) - Implies that the missingness of a field can be explained by the values in other columns, but not from that column.
Missing NOT at Random (MNAR) - Implies whether there was a reason why the respondent didn’t fill up that field, and hence that data is not missing at random. For example, if someone is obese, they are less likely to disclose their weight.