I am using h2o version 3.10.4.8.
library(magrittr)
library(h2o)
h2o.init(nthreads = -1, max_mem_size = "6g")
data.url <- "https://raw.githubusercontent.com/DarrenCook/h2o/bk/datasets/"
iris.hex <- paste0(data.url, "iris_wheader.csv") %>%
h2o.importFile(destination_frame = "iris.hex")
y <- "class"
x <- setdiff(names(iris.hex), y)
model.glm <- h2o.glm(x, y, iris.hex, family = "multinomial")
preds <- h2o.predict(model.glm, iris.hex)
h2o.confusionMatrix(model.glm)
h2o.table(preds["predict"])
This is the output of h2o.confusionMatrix(model.glm):
Confusion Matrix: vertical: actual; across: predicted
Iris-setosa Iris-versicolor Iris-virginica Error Rate
Iris-setosa 50 0 0 0.0000 = 0 / 50
Iris-versicolor 0 48 2 0.0400 = 2 / 50
Iris-virginica 0 1 49 0.0200 = 1 / 50
Totals 50 49 51 0.0200 = 3 / 150
Since it says across:predicted, I interpret this to mean that the model made 50 (0 + 48 + 2) predictions that are Iris-versicolor.
This is the output of h2o.table(preds["predict"]):
predict Count
1 Iris-setosa 50
2 Iris-versicolor 49
3 Iris-virginica 51
This tells me that the model made 49 predictions that are Iris-versicolor.
Is the confusion matrix incorrectly labelled or did I make a mistake in interpreting the results?