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What is an acceptable amount of missing data?
@shuvayan – Theoretically, 25 to 30% is the maximum missing values are allowed, beyond which we might want to drop the variable from analysis. Practically this varies.At times we get variables with ~50% of missing values but still the customer insist to have it for analyzing.
How do you use regression for imputation?
With regression imputation the information of other variables is used to predict the missing values in a variable by using a regression model. Commonly, first the regression model is estimated in the observed data and subsequently using the regression weights the missing values are predicted and replaced.
Do you need a regression model for missing data imputation?
Regression Imputation assumes that the data is Missing At Random, more about it can be found in the refereneces below. For a better Regression model, we might have to follow different Data Transformation methods depending on our data.
How to impute data in a deterministic regression?
Now, let’s apply a deterministic regression imputation to our example data. The function mice () is used to impute the data; method = “norm.predict” is the specification for deterministic regression imputation; and m = 1 specifies the number of imputed data sets (in our case single imputation).
Why is missing data imputation used in Kaggle?
Well, that would be one method of handling missing values called Complete Case Analysis, something which is (very) rarely used. The obvious reason being that, if we delete the data point containing missing data ( List wise deletion ), we will end up with a small number of samples to train our learning model and thus accuracy would be of concern.
Which is better, regression imputation or mean imputation?
Relationships of X and Y (i.e. correlations, regression coefficients etc.) are preserved, since imputed values are based on regression models. This is a big advantage over simpler imputation methods such as mean imputation or zero substitution.