bootImportance {h2otools} | R Documentation |
Bootstrap Variable Importance And Averaged Grid Variable Importance
Description
Evaluates variable importance as well as bootstrapped variable importance for a single model or a grid of models
Usage
bootImportance(model, df, metric, n = 100)
Arguments
model |
a model or a model grid of models trained by h2o machine learning software |
df |
dataset for testing the model. if "n" is bigger than 1, this dataset will be used for drawing bootstrap samples. otherwise (default), the entire dataset will be used for evaluating the model |
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)
[Package h2otools version 0.4 Index]