Description Usage Arguments Details Value See Also Examples
Retrieve either a single or many confusion matrices from H2O objects.
1 2 3 4 5 6 7 8 | 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.
1 2 3 4 5 6 7 8 9 10 | library(h2o)
h2o.init()
prosPath <- system.file("extdata", "prostate.csv", package="h2o")
hex <- h2o.uploadFile(prosPath)
hex[,2] <- as.factor(hex[,2])
model <- h2o.gbm(x = 3:9, y = 2, training_frame = hex, distribution = "bernoulli")
h2o.confusionMatrix(model, hex)
# Generating a ModelMetrics object
perf <- h2o.performance(model, hex)
h2o.confusionMatrix(perf)
|
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