h2o: h2o.make_metrics – R documentation

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h2o.make_metrics

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,
  auc_type = "NONE"
)

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.
`auc_type`	(optional) For multinomial classification you have to specify which type of agregated AUC/AUCPR will be used to calculate this metric.

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

R Interface for the 'H2O' Scalable Machine Learning Platform

v3.32.1.2

Apache License (== 2.0)

Authors

Erin LeDell [aut, cre], Navdeep Gill [aut], Spencer Aiello [aut], Anqi Fu [aut], Arno Candel [aut], Cliff Click [aut], Tom Kraljevic [aut], Tomas Nykodym [aut], Patrick Aboyoun [aut], Michal Kurka [aut], Michal Malohlava [aut], Ludi Rehak [ctb], Eric Eckstrand [ctb], Brandon Hill [ctb], Sebastian Vidrio [ctb], Surekha Jadhawani [ctb], Amy Wang [ctb], Raymond Peck [ctb], Wendy Wong [ctb], Jan Gorecki [ctb], Matt Dowle [ctb], Yuan Tang [ctb], Lauren DiPerna [ctb], Tomas Fryda [ctb], H2O.ai [cph, fnd]

Initial release

2021-04-29