wehub-resource-sync
18 lines
1.2 KiB
Markdown
18 lines
1.2 KiB
Markdown
# What are some common approaches for dealing with missing data?
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Many different approaches exist for dealing with missing values; I'd roughly categorize our options into a) deletion and b) imputation techniques.
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## a) Deletion
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1) We have a lot of training samples and can afford deleting some of those. Here, we can simply remove samples with missing feature values from the dataset entirely.
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2) We have a large number of feature columns and some of them are redundant. Relatively many samples have a missing feature value in a certain column. In this scenario, it may be a good idea to remove these feature columns with missing values entirely.
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## b) Imputation
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If we can't afford deleting data points, we could use imputation techniques to "guess" placeholder values from the remaining data points.
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1) The simplest imputation technique may be the replacement of a missing feature value by its feature column's mean (median or mode).
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2) Instead of replacing a feature value by its column mean, we can only consider the k-nearest neighbors of this datapoint for computing the mean (median or mode) -- we identify the neighbors based on the remaining feature columns that don't have missing values.
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