Contents
Is Random Forest robust to missing values?
One of the main advantages of Random Forest Algorithm is that it can perform the best to handle the MCAR and MAR values , and it can maintain accuracy for missing data. It adapts to the data structure taking into consideration of the high variance or the bias, producing better results on large datasets.
Can Random Forest take null values?
One particular family of models we use is Random Forest Classifiers (RFCs). A RFC is a collection of trees, each independently grown using labeled and complete input training data. By complete we explicitly mean that there are no missing values i.e. NULL or NaN values.
Can adaboost handle missing values?
Bayesian methods have traditionally been used to cope with missing data, with boosting being applied to the output of Bayesian classifiers. We explore a variation of Adaboost that deals with the missing values at the level of the boosting algorithm itself, without the need for any density estimation step.
Is there a random forest missing data algorithm?
A detailed comparison of the different forest missing data algorithms implemented using “impute” was described in a recent paper with Fei Tang “Random forest missing data algorithms”, 2017. I recommend consulting the help files of “rfsrc” and “impute” from randomForestSRC for more details about imputation and OTFI.
Why does random forest not randomly select variables?
RF randomly selects variables when splitting a node and, as such, variables within a node may be uncorrelated, and a reasonable surrogate split may not exist. Another concern is that surrogate splitting alters the interpretation of a variable, which affects measures such as [Variable Importance].
How does cart handle missing values in predictors?
For CART, you can apply the missing-in-attributes (MIA) approach. That is, for categorical predictors, you code missing as a separate category. For numerical predictors, you create two new variables for every variable with missings: one where you code missings as -Inf and one where you code missings as +Inf.
Why are surrogate splits not meaningful in random forest?
Further, surrogate splits may not even be meaningful in a forest paradigm. RF randomly selects variables when splitting a node and, as such, variables within a node may be uncorrelated, and a reasonable surrogate split may not exist.