keep_cross_validation_models

  • Available in: GBM, DRF, Deep Learning, GLM, GAM, Naïve-Bayes, K-Means, XGBoost, AutoML

  • Hyperparameter: yes for AutoML, no for other algorithms

Description

N-fold cross-validation is used to validate a model internally, i.e., to estimate the model performance without having to sacrifice a validation split. When building cross-validated models, H2O builds nfolds+1 models: nfolds cross-validated models and 1 overarching model over all of the training data. For example, if you specify nfolds=5, then 6 models are built. The first 5 models are the cross-validation models and are built on 80% of the training data. You can save each of these models for further inspection by enabling the keep_cross_validation_models option. Note that this option is enabled by default except for AutoML.

More information is available in the Cross-Validation section.

Example

R

Python

library(h2o)
h2o.init()

# import the cars dataset:
# this dataset is used to classify whether or not a car is economical based on
# the car's displacement, power, weight, and acceleration, and the year it was made
cars <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

# convert response column to a factor
cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"])

# set the predictor names and the response column name
predictors <- c("displacement", "power", "weight", "acceleration", "year")
response <- "economy_20mpg"

# split into train and validation sets
cars_split <- h2o.splitFrame(data = cars, ratios = 0.8, seed = 1234)
train <- cars_split[[1]]
valid <- cars_split[[2]]

# try using the `keep_cross_validation_models` (boolean parameter):
# train your model, set nfolds parameter
cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train,
                    nfolds = 5, keep_cross_validation_models = TRUE, seed = 1234)

# retrieve the list of cross-validation models
cars_gbm_cv_models <- h2o.cross_validation_models(cars_gbm)

import h2o
from h2o.estimators.gbm import H2OGradientBoostingEstimator
h2o.init()

# import the cars dataset:
# this dataset is used to classify whether or not a car is economical based on
# the car's displacement, power, weight, and acceleration, and the year it was made
cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

# convert response column to a factor
cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()

# set the predictor names and the response column name
predictors = ["displacement","power","weight","acceleration","year"]
response = "economy_20mpg"

# split into train and validation sets
train, valid = cars.split_frame(ratios = [.8], seed = 1234)

# try using the `keep_cross_validation_models` (boolean parameter):
# first initialize your estimator, set nfolds parameter
cars_gbm = H2OGradientBoostingEstimator(keep_cross_validation_models = True, nfolds = 5, seed = 1234)

# then train your model
cars_gbm.train(x = predictors, y = response, training_frame = train)

# retrieve the cross-validation models
cars_gbm_cv_models = cars_gbm.cross_validation_models()