h2o (version 3.24.0.5)

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,
  ...)
# S4 method for 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.

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.

Details

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

See Also

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

Examples

# 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)
# }