6 Different Ways to Compensate for Missing Data (Data Imputation with examples)
Popular strategies to statistically impute missing values in a dataset.
Zero or Constant imputation - as the name suggests - it replaces the missing values with either zero or any constant value you specify
Pros:
Quite accurate compared to other methods.
It has some functions that can handle categorical data (Feature Encoder).
It supports CPUs and GPUs.
Cons:
Single Column imputation.
Can be quite slow with large datasets.
You have to specify the columns that contain information about the target column that will be imputed.
Other Imputation Methods:
Stochastic regression imputation:
It is quite similar to regression imputation which tries to predict the missing values by regressing it from other related variables in the same dataset plus some random residual value.
Extrapolation and Interpolation:
It tries to estimate values from other observations within the range of a discrete set of known data points.
Hot-Deck imputation:
Works by randomly choosing the missing value from a set of related and similar variables.
In conclusion, there is no perfect way to compensate for the missing values in a dataset. Each strategy can perform better for certain datasets and missing data types but may perform much worse on other types of datasets. There are some set rules to decide which strategy to use for particular types of missing values, but beyond that, you should experiment and check which model works best for your dataset.
References:
[1] Buuren, S. V., & Groothuis-Oudshoorn, K. (2011). Mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software
