Random Forests – Missing Data Leave a reply Welcome to your Random Forests - Missing Data What is the first step for scoring new observations with missing data?Use a random forest to predict the missing valuesMedian imputation on the missing valuesCreate copies of the data with the possible values for the targetProximity used in Random Forest imputation is based onHow often two observations are put into the same leaf nodeHow many features the two observations have in commonEuclidean distanceWhat method does a random forest use to make an intial guess for missing values in the training data?The median valueProximity matrixThe mean valueAfter the initial guess for missing values in the training data, the missing value is then imputed based on theMost frequent non-missing value where frequency is weighted by proximityThe missing values are given a new categorical value that codes for missingPlease fill in the comment box below. Time is Up! Time's up
