compute_metrics

  • Available in: Naïve-Bayes, PCA

  • Hyperparameter: yes

Description

The compute_metrics option specifies to compute metrics on the training data. This option is enabled by default.

Example

R

Python

library(h2o)
h2o.init()

# import the prostate dataset:
prostate <- h2o.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")

# Converting CAPSULE, RACE, DCAPS, and DPROS to categorical
prostate$CAPSULE <- as.factor(prostate$CAPSULE)
prostate$RACE <- as.factor(prostate$RACE)
prostate$DCAPS <- as.factor(prostate$DCAPS)
prostate$DPROS <- as.factor(prostate$DPROS)

# Compare with Naive Bayes when x = 3:9, y = 2, and do not compute metrics
prostate_nb <- h2o.naiveBayes(x = 3:9, y = 2, training_frame = prostate, laplace = 0, compute_metrics = FALSE)
print(prostate_nb) # Note that metrics are not computed and, thus, do not display.

# Predict on training data
prostate_pred <- predict(prostate_nb, prostate)
print(head(prostate_pred))

import h2o
h2o.init()
from h2o.estimators.naive_bayes import H2ONaiveBayesEstimator

# import prostate dataset:
prostate = h2o.import_file("http://s3.amazonaws.com/h2o-public-test-data/smalldata/prostate/prostate.csv.zip")

# Converting CAPSULE, RACE, DCAPS, and DPROS to categorical, and set the response column
prostate['CAPSULE'] = prostate['CAPSULE'].asfactor()
prostate['RACE'] = prostate['RACE'].asfactor()
prostate['DCAPS'] = prostate['DCAPS'].asfactor()
prostate['DPROS'] = prostate['DPROS'].asfactor()
response_col = 'CAPSULE'

# Compare with Naive Bayes when x = 3:9, y = 2, and do not compute metrics
prostate_nb = H2ONaiveBayesEstimator(laplace = 0, compute_metrics = False)
prostate_nb.train(x=list(range(3,9)), y=response_col, training_frame=prostate)
prostate_nb.show() # Note that metrics are not computed and, thus, do not display.

# Predict on training data
prostate_pred = prostate_nb.predict(prostate)
prostate_pred.head()