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What is the best imputation method you would consider for replacing missing values in a numerical features?
Mean imputation. Perhaps the easiest way to impute is to replace each missing value with the mean of the observed values for that variable.
Does outlier treatment come first or missing value imputation?
First Outlier Treatment and then Missing Data Imputation Reason is that the outliers will also influence the missing data algorithms in a negative manner.
Should I impute missing values?
One way to handle this problem is to get rid of the observations that have missing data. However, you will risk losing data points with valuable information. A better strategy would be to impute the missing values. In other words, we need to infer those missing values from the existing part of the data.
Which model can handle outliers?
Common Methods for Detecting Outliers. When detecting outliers, we are either doing univariate analysis or multivariate analysis. When your linear model has a single predictor, then you can use univariate analysis.
Why do we need imputation of missing data?
If the missing values truly are missing at random, maybe it does not matter much, but generally missingness might depend on other observed variables, and then estimates of mean and scale could be skewed by this pattern in the missingness. Imputation (better multiple imputation) is a way to fight this skewing.
When to center and scale data before or After imputation?
When you use method = knnImpute, it first center and scale the data before imputation. preProcValues <- preProcess (data, method = c (“knnImpute”,”center”,”scale”)) Presumably, if you really need to center & scale the data, that should be done after imputation, as the imputation could influence on the correct center and scale to use!
Which is the first step in an imputation?
Generally, the imputation should be the very first step in any analysis you do. You say that imputation should preserve center & scale, and also standardization. Why?