bootPerformance: bootPerformance

In h2otools: Machine Learning Model Evaluation for 'h2o' Package

bootPerformance

Description

Evaluate model performance by bootstrapping from training dataset

Usage

bootPerformance(model, df, metric, n = 100)

Arguments

model	a model trained by h2o machine learning software
df	training, validation, or testing dataset to bootstrap from
metric	character. model evaluation metric to be passed to boot R package. this could be, for example "AUC", "AUCPR", RMSE", etc., depending of the model you have trained. all evaluation metrics provided for your H2O models can be specified here.
n	number of bootstraps

Value

list of mean perforance of the specified metric and other bootstrap results

Author(s)

E. F. Haghish

Examples

## Not run: 
library(h2o)
h2o.init(ignore_config = TRUE, nthreads = 2, bind_to_localhost = FALSE, insecure = TRUE)
prostate_path <- system.file("extdata", "prostate.csv", package = "h2o")
df <- read.csv(prostate_path)

# prepare the dataset for analysis before converting it to h2o frame.
df$CAPSULE <- as.factor(df$CAPSULE)

# convert the dataframe to H2OFrame and run the analysis
prostate.hex <- as.h2o(df)
aml <- h2o.automl(y = "CAPSULE", training_frame = prostate.hex, max_runtime_secs = 30)

# evaluate the model performance
perf <- h2o.performance(aml@leader, xval = TRUE)

# evaluate bootstrap performance for the training dataset
#    NOTE that the raw data is given not the 'H2OFrame'
perf <- bootPerformance(model = aml@leader, df = df, metric = "RMSE", n = 500)

## End(Not run)

h2otools documentation built on April 4, 2025, 2:33 a.m.