What do missing values do in a dataset?
If the missing values in a column or feature are numerical, the values can be imputed by the mean of the complete cases of the variable. Mean can be replaced by median if the feature is suspected to have outliers. For a categorical feature, the missing values could be replaced by the mode of the column.
How many imputations are needed?
An old answer is that 2 to 10 imputations usually suffice, but this recommendation only addresses the efficiency of point estimates. You may need more imputations if, in addition to efficient point estimates, you also want standard error (SE) estimates that would not change (much) if you imputed the data again.
What causes missing values in a dataset?
The cause of missing values can be data corruption or failure to record data. The handling of missing data is very important during the preprocessing of the dataset as many machine learning algorithms do not support missing values. There are various strategies to handle missing values in a dataset including the prediction of missing values.
How to treat missing values in your data?
In such a case, one won’t be deleting any observation. Each of the samples will ignore the variable which has the missing value in it. Both the above methods suffer from loss of information.
Why are there missing values in machine learning?
The real-world data often has a lot of missing values. The cause of missing values can be data corruption or failure to record data. The handling of missing data is very important during the preprocessing of the dataset as many machine learning algorithms do not support missing values.
When to remove missing values from a model?
Another reason is that when using your model in production, the model will not automatically know how to handle missing data. A couple rules of thumb to follow when using this method: Rows of missing values can be removed when the NULL values (missing values) are around 5% (or less) of the total data.