I cannot find out how the variable importance for classification problems is calculated in
h2o. There is a Stackoverflow question asking the same, but the accepted answer does not help (keeps referring to "squared error" where I would expect "accuracy" or "gini impurity" being used; same for the linked paper in that SO thread).
h2o seems to use a different methodology for calculating variable importance than the usual permutation approach, see the h2o documentation
How is variable importance calculated for DRF? Variable importance is determined by calculating the relative influence of each variable: whether that variable was selected during splitting in the tree building process and how much the squared error (over all trees) improved as a result.
So, I tried to figure out how
h2o calculates variable importance myself. Here a simple single-tree example (using all data for training)
library(h2o) data(iris) h2o.init() irisSimple=iris irisSimple$Species=factor(ifelse(irisSimple$Species=="virginica", "virginica","other")) mdl=h2o.randomForest(x=setdiff(colnames(irisSimple),"Species"), y="Species",training_frame=as.h2o(irisSimple), sample_rate=1.0,ntrees=1,seed=1)
We can look into the single tree via exporting to a POJO
Now extract and print the first split node
pojo[grepl("double pred = ",pojo)] #double pred = (Double.isNaN(data) || data[3 /* Petal.Width */] <1.75f ?
Calculate left (
true) and right (
false) data bins
Finally calculate accuracy increase
rootCorrect=max(table(irisSimple$Species)) lCorrect=max(table(lBin$Species)) rCorrect=max(table(rBin$Species)) accIncrease=(lCorrect+rCorrect-rootCorrect)/nrow(iris) accIncrease # 0.29333
and compare to the
h2o.varimp(mdl) #Variable Importances: # variable relative_importance scaled_importance percentage #1 Petal.Width 28.585253 1.000000 0.857558 #2 Petal.Length 3.081414 0.107797 0.092442 #3 Sepal.Width 1.000000 0.034983 0.030000 #4 Sepal.Length 0.666667 0.023322 0.020000
33.33 indicating that
relative_importance refers to the accuracy increase (the naive model assigning "other" to all observations has 50 observations wrong; the decision tree gets all 150 observations right).
As you can see, my calculated accuracy increase of
0.29333 for the
Petal.Width split point is larger than the
h2o value of
So, I am wondering what numbers
h2o is reporting...
packageVersion("h2o") # ‘22.214.171.124’
sample_rate=1.0and do not provide a validation frame? $\endgroup$
Speciesis my response variable. $\endgroup$