h2o.make_metrics: Create Model Metrics from predicted and actual values in H2O

In h2o: R Interface for the 'H2O' Scalable Machine Learning Platform

Create Model Metrics from predicted and actual values in H2O

Description

Given predicted values (target for regression, class-1 probabilities or binomial or per-class probabilities for multinomial), compute a model metrics object

Usage

h2o.make_metrics(
  predicted,
  actuals,
  domain = NULL,
  distribution = NULL,
  weights = NULL,
  treatment = NULL,
  auc_type = "NONE",
  auuc_type = "AUTO",
  auuc_nbins = -1
)

Arguments

predicted	An H2OFrame containing predictions
actuals	An H2OFrame containing actual values
domain	Vector with response factors for classification.
distribution	Distribution for regression.
weights	(optional) An H2OFrame containing observation weights.
treatment	(optional, for uplift models only) An H2OFrame containing treatment column for uplift classification.
auc_type	(optional) For multinomial classification you have to specify which type of agregated AUC/AUCPR will be used to calculate this metric.
auuc_type	(optional) For uplift binomial classification you have to specify which type of AUUC will be used to calculate this metric. Possibilities are gini, lift, gain, AUTO. Default is AUTO which means qini.
auuc_nbins	(optional) For uplift binomial classification you have to specify number of bins to be used for calculation the AUUC. Default is -1, which means 1000.

Value

Returns an object of the H2OModelMetrics subclass.

Examples

## Not run: 
library(h2o)
h2o.init()
prostate_path <- system.file("extdata", "prostate.csv", package = "h2o")
prostate <- h2o.uploadFile(path = prostate_path)
prostate$CAPSULE <- as.factor(prostate$CAPSULE)
prostate_gbm <- h2o.gbm(3:9, "CAPSULE", prostate)
pred <- h2o.predict(prostate_gbm, prostate)[, 3] ## class-1 probability
h2o.make_metrics(pred, prostate$CAPSULE)

## End(Not run)

h2o documentation built on Aug. 9, 2023, 9:06 a.m.