Transform a Dataset using Trained H2O4GPU Estimator
This function transforms the given new data using a trained H2O4GPU model.
## S3 method for class 'h2o4gpu_model' transform(object, x, ...)
object |
The h2o4gpu model object |
x |
The new data where each column represents a different predictor variable to be used in generating predictions. |
... |
Additional arguments (unused for now). |
## Not run: library(h2o4gpu) # Prepare data iris$Species <- as.integer(iris$Species) # convert to numeric data # Randomly sample 80% of the rows for the training set set.seed(1) train_idx <- sample(1:nrow(iris), 0.8*nrow(iris)) train <- iris[train_idx, ] test <- iris[-train_idx, ] # Train a K-Means model model_km <- h2o4gpu.kmeans(n_clusters = 3L) %>% fit(train) # Transform test data test_dist <- model_km %>% transform(test) ## End(Not run)
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