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Given predicted values (target for regression, class-1 probabilities or binomial or per-class probabilities for multinomial), compute a model metrics object
h2o.make_metrics(
predicted,
actuals,
domain = NULL,
distribution = NULL,
weights = NULL
)
An H2OFrame containing predictions
An H2OFrame containing actual values
Vector with response factors for classification.
Distribution for regression.
(optional) An H2OFrame containing observation weights.
# 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)
# }
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