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How does Lightgbm deal with missing values?
LIGHTGBM will ignore missing values during a split, then allocate them to whichever side reduces the loss the most. Section 3.2 of this reference explains it.
What are the methods of handling missing values?
By far the most common approach to the missing data is to simply omit those cases with the missing data and analyze the remaining data. This approach is known as the complete case (or available case) analysis or listwise deletion.
When missing values should be handled?
Missing values can be handled by deleting the rows or columns having null values. If columns have more than half of the rows as null then the entire column can be dropped. The rows which are having one or more columns values as null can also be dropped.
Does regression ignore missing values?
Linear Regression The variable with missing data is used as the dependent variable. Cases with complete data for the predictor variables are used to generate the regression equation; the equation is then used to predict missing values for incomplete cases.
How are missing values handled in machine learning?
The default behavior allows the missing values to be sent down either branch of a split. Replacing with a negative value that is less than all your data forces the (originally) missing values to take the left branch, and so your model has (slightly) less capacity.
What happens if there are no missing values during prediction?
The only reference I saw in the documentation regarding missing values was: According to those sources, nulls are allocated to the bins that reduce the loss during training. Is that true? And if so, what if there are no missing values in training? of course the prediction have all missing value handles.
How are missing values tested in numerical features?
For numerical features, both left side and right side are tested for missing values. For categorical features, as there are not “left” and “right”, and we can put anything to “left”. Therefore, the missing values are in right side always. Sorry @guolinke , let me try to articulate a bit better!
Why are missing values always on the right side?
“by default” doesn’t means they are always in that way. For numerical features, both left side and right side are tested for missing values. For categorical features, as there are not “left” and “right”, and we can put anything to “left”. Therefore, the missing values are in right side always.