``keep_cross_validation_predictions`` ------------------------------------- - Available in: GBM, DRF, Deep Learning, GLM, Naïve-Bayes, K-Means - Hyperparameter: no 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. Each cross-validated model produces a prediction frame pertaining to its fold. You can save each of these prediction frames by enabling the ``keep_cross_validation_predictions`` option. Note that this option is disabled by default. More information is available in the `Cross-Validation <../../cross-validation.html>`__ section. Related Parameters ~~~~~~~~~~~~~~~~~~ - `keep_cross_validation_fold_assignment `__ - `nfolds `__ Example ~~~~~~~ .. example-code:: .. code-block:: r 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_predictions` (boolean parameter): # train your model, set nfolds parameter cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, nfolds = 5, keep_cross_validation_predictions= TRUE, seed = 1234) # print the cross-validation predictions h2o.cross_validation_predictions(cars_gbm) .. code-block:: python 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_predictions` (boolean parameter): # first initialize your estimator, set nfolds parameter cars_gbm = H2OGradientBoostingEstimator(keep_cross_validation_predictions = True, nfolds = 5, seed = 1234) # then train your model cars_gbm.train(x = predictors, y = response, training_frame = train) # print the cross-validation predictions cars_gbm.cross_validation_predictions()