Plot an H2O Model
Plots training set (and validation set if available) scoring history for an H2O Model
## S3 method for class 'H2OModel' plot(x, timestep = "AUTO", metric = "AUTO", ...)
x |
A fitted H2OModel object for which the scoring history plot is desired. |
timestep |
A unit of measurement for the x-axis. |
metric |
A unit of measurement for the y-axis. |
... |
additional arguments to pass on. |
This method dispatches on the type of H2O model to select the correct
scoring history. The timestep and metric arguments are restricted to what is
available in the scoring history for a particular type of model.
Returns a scoring history plot.
h2o.deeplearning, h2o.gbm,
h2o.glm, h2o.randomForest for model
generation in h2o.
## Not run:
if (requireNamespace("mlbench", quietly=TRUE)) {
library(h2o)
h2o.init()
df <- as.h2o(mlbench::mlbench.friedman1(10000, 1))
rng <- h2o.runif(df, seed = 1234)
train <- df[rng < 0.8,]
valid <- df[rng >= 0.8,]
gbm <- h2o.gbm(x = 1:10, y = "y", training_frame = train, validation_frame = valid,
ntrees = 500, learn_rate = 0.01, score_each_iteration = TRUE)
plot(gbm)
plot(gbm, timestep = "duration", metric = "deviance")
plot(gbm, timestep = "number_of_trees", metric = "deviance")
plot(gbm, timestep = "number_of_trees", metric = "rmse")
plot(gbm, timestep = "number_of_trees", metric = "mae")
}
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.