SiMI imputes numerical and categorical missing values by making an educated guess based on records that are similar to the record having a missing value. Using the similarity and correlations, missing values are then imputed. To achieve a higher quality of imputation some segments are merged together using a novel approach.
data-science
linear-regression
dataset
missing-data
preprocessing
data-cleaning
decision-tree
decision-tree-classifier
missing-values
decision-forest
decision-forest-algorithm
missing-value-handling
missing-data-imputation
missing-value-imputation
numerical-missing-value
categorical-missing-value
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Updated
Mar 24, 2023 - Java