Retrieve either a single or many confusion matrices from H2O objects.
h2o.confusionMatrix(object, ...) # S4 method for H2OModel h2o.confusionMatrix(object, newdata, valid = FALSE, ...) # S4 method for 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. |
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.
The H2OModelMetrics version of this function will only take H2OBinomialMetrics or H2OMultinomialMetrics objects. If no threshold is specified, all possible thresholds are selected.
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) # }