h2o.confusionMatrix: Access H2O Confusion Matrices

Description

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

Usage

h2o.confusionMatrix(object, ...)
# S4 method for H2OModel
h2o.confusionMatrix(object, newdata, valid = FALSE, xval = FALSE, ...)
# S4 method for H2OModelMetrics
h2o.confusionMatrix(object, thresholds = NULL, metrics = NULL)

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.

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.
xval: Retrieve the cross-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.

Examples

Run this code

if (FALSE) {
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)
}