fold_column.rst

fold_column

• Available in: GBM, DRF, Deep Learning, GLM, GAM, Naïve-Bayes, K-Means, AutoML, XGBoost
• Hyperparameter: no

Description

When performing N-fold cross validation, you can specify to split the data into subsets using a fold_assignment. A fold assignment is suitable for datasets that are i.i.d.

If your dataset requires custom grouping to perform meaningful cross-validation, then a "fold column" should be created and provided instead. The fold_column option specifies the column in the dataset that contains the cross-validation fold index assignment per observation. The fold column can include integers (0, 1, 2, ... , N-1 values or 1, 2, 3... , N values) or categorical values. When specified, the algorithm uses that column’s values to split the data into subsets.

Notes

• The fold column must exist in the training data.
• The fold column that is specified cannot be specified in ignored_columns, response_colum, weights_column or offset_column.

Related Parameters

• fold_assignment
• nfolds

Example

.. tabs::
   .. code-tab:: r 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"

                # create a fold column with 5 folds
                # randomly assign fold numbers 0 through 4 for each row in the column
                fold_numbers <- h2o.kfold_column(cars, nfolds = 5)

                # rename the column "fold_numbers"
                names(fold_numbers) <- "fold_numbers"

                # print the fold_assignment column
                print(fold_numbers)

                # append the fold_numbers column to the cars dataset
                cars <- h2o.cbind(cars, fold_numbers)

                # try using the fold_column parameter:
                cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = cars,
                                    fold_column="fold_numbers", seed = 1234)

                # print the auc for your model
                print(h2o.auc(cars_gbm, xval = TRUE))

   .. code-tab:: python

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

                # 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"

                # create a fold column with 5 folds
                # randomly assign fold numbers 0 through 4 for each row in the column
                fold_numbers = cars.kfold_column(n_folds = 5, seed = 1234)

                # rename the column "fold_numbers"
                fold_numbers.set_names(["fold_numbers"])

                # append the fold_numbers column to the cars dataset
                cars = cars.cbind(fold_numbers)

                # print the fold_assignment column
                print(cars['fold_numbers'])

                # initialize the estimator then train the model
                cars_gbm = H2OGradientBoostingEstimator(seed = 1234)
                cars_gbm.train(x=predictors, y=response, training_frame=cars, fold_column="fold_numbers")

                # print the auc for the cross-validated data
                cars_gbm.auc(xval=True)