Learning Curve Plot — h2o.learning_curve

Create learning curve plot for an H2O Model. Learning curves show error metric dependence on learning progress, e.g., RMSE vs number of trees trained so far in GBM. There can be up to 4 curves showing Training, Validation, Training on CV Models, and Cross-validation error.

h2o.learning_curve_plot(
  model,
  metric = c("AUTO", "auc", "aucpr", "mae", "rmse", "anomaly_score", "convergence",
    "custom", "custom_increasing", "deviance", "lift_top_group", "logloss",
    "misclassification", "negative_log_likelihood", "objective", "sumetaieta02",
    "loglik"),
  cv_ribbon = NULL,
  cv_lines = NULL
)

Arguments

model	an H2O model
metric	Metric to be used for the learning curve plot. These should mostly correspond with stopping metric.
cv_ribbon	if True, plot the CV mean as a and CV standard deviation as a ribbon around the mean, if NULL, it will attempt to automatically determine if this is suitable visualisation
cv_lines	if True, plot scoring history for individual CV models, if NULL, it will attempt to automatically determine if this is suitable visualisation

Value

A ggplot2 object

Examples

if (FALSE) {
library(h2o)
h2o.init()

# Import the wine dataset into H2O:
f <- "https://h2o-public-test-data.s3.amazonaws.com/smalldata/wine/winequality-redwhite-no-BOM.csv"
df <-  h2o.importFile(f)

# Set the response
response <- "quality"

# Split the dataset into a train and test set:
splits <- h2o.splitFrame(df, ratios = 0.8, seed = 1)
train <- splits[[1]]
test <- splits[[2]]

# Build and train the model:
gbm <- h2o.gbm(y = response,
               training_frame = train)

# Create the learning curve plot
learning_curve <- h2o.learning_curve_plot(gbm)
print(learning_curve)
}