In most of the predictive model techniques, it is required to impute missing values before training a predictive model. There is a way you can impute missing data with Random Forest Algorithm.
I. Impute missing values in predictor data using proximity from randomForest.
The proximity matrix from the randomForest is used to update the imputation of the NAs. For continuous predictors, the imputed value is the weighted average of the non-missing obervations, where the weights are the proximities. For categorical predictors, the imputed value is the category with the largest average proximity. This process is iterated iter times.
Default Method : 5 Iterations and 300 Trees
II. Impute missing values in predictor data using median / mode.
Median for numeric variables
Mode for categorical variables
data(iris)
str(iris)
iris.na <- iris
set.seed(111)
## artificially drop some data values.
for (i in 1:4) iris.na[sample(150, sample(20)), i] <- NA
set.seed(222)
library(randomForest)
iris.imputed <- rfImpute(Species ~ ., iris.na, iter=5, ntree=500)
set.seed(333)
iris.rf <- randomForest(Species ~ ., iris.imputed)
print(iris.rf)
II. Impute missing values in predictor data using median / mode.
data(iris)Note :
iris.na <- iris
set.seed(111)
## artificially drop some data values.
for (i in 1:4) iris.na[sample(150, sample(20)), i] <- NA
library(randomForest)
iris.roughfix <- na.roughfix(iris.na)
iris.narf <- randomForest(Species ~ ., iris.na, na.action=na.roughfix)
print(iris.narf)
Median for numeric variables
Mode for categorical variables
Deepanshu founded ListenData with a simple objective - Make analytics easy to understand and follow. He has over 10 years of experience in data science. During his tenure, he worked with global clients in various domains like Banking, Insurance, Private Equity, Telecom and HR.
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