Access H2O Confusion Matrices
Retrieve either a single or many confusion matrices from H2O objects.
h2o.confusionMatrix(object, ...) ## S4 method for signature 'H2OModel' h2o.confusionMatrix(object, newdata, valid = FALSE, ...) ## S4 method for signature 'H2OModelMetrics' h2o.confusionMatrix(object, thresholds = NULL, metrics = NULL)
object |
Either an H2OModel object or an H2OModelMetrics object. |
... |
Extra arguments for extracting train or valid confusion matrices. |
newdata |
An H2OFrame object that can be scored on. Requires a valid response column. |
valid |
Retrieve the validation metric. |
thresholds |
(Optional) A value or a list of valid values between 0.0 and 1.0. This value is only used in the case of H2OBinomialMetrics objects. |
metrics |
(Optional) A metric or a list of valid metrics ("min_per_class_accuracy", "absolute_mcc", "tnr", "fnr", "fpr", "tpr", "precision", "accuracy", "f0point5", "f2", "f1"). This value is only used in the case of H2OBinomialMetrics objects. |
The H2OModelMetrics version of this function will only take H2OBinomialMetrics or H2OMultinomialMetrics objects. If no threshold is specified, all possible thresholds are selected.
Calling this function on H2OModel objects returns a
confusion matrix corresponding to the predict function.
If used on an H2OBinomialMetrics object, returns a list
of matrices corresponding to the number of thresholds specified.
predict for generating prediction frames,
h2o.performance for creating
H2OModelMetrics.
## Not run:
library(h2o)
h2o.init()
prostate_path <- system.file("extdata", "prostate.csv", package = "h2o")
prostate <- h2o.uploadFile(prostate_path)
prostate[, 2] <- as.factor(prostate[, 2])
model <- h2o.gbm(x = 3:9, y = 2, training_frame = prostate, distribution = "bernoulli")
h2o.confusionMatrix(model, prostate)
# Generating a ModelMetrics object
perf <- h2o.performance(model, prostate)
h2o.confusionMatrix(perf)
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.