h2o.confusionMatrix: Access H2O Confusion Matrices

Description Usage Arguments Details Value See Also Examples

Description

Retrieve either a single or many confusion matrices from H2O objects.

Usage

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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)

Arguments

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.

Details

The H2OModelMetrics version of this function will only take H2OBinomialMetrics or H2OMultinomialMetrics objects. If no threshold is specified, all possible thresholds are selected.

Value

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.

See Also

predict for generating prediction frames, h2o.performance for creating H2OModelMetrics.

Examples

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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)

h2o documentation built on Dec. 2, 2017, 1:04 a.m.