Regression H2O Deep Learning Learner
Source:R/learner_h2o_regr_deeplearning.R
mlr_learners_regr.h2o.deeplearning.RdRegression feed-forward multilayer artificial neural network learner.
Class h2o::h2o.deeplearning() from package h2o.
Meta Information
Task type: “regr”
Predict Types: “response”
Feature Types: “integer”, “numeric”, “factor”
Required Packages: mlr3, mlr3extralearners, h2o
Parameters
| Id | Type | Default | Levels | Range |
| activation | character | Rectifier | Rectifier, Tanh, TanhWithDropout, RectifierWithDropout, Maxout, MaxoutWithDropout | - |
| adaptive_rate | logical | TRUE | TRUE, FALSE | - |
| autoencoder | logical | FALSE | TRUE, FALSE | - |
| average_activation | numeric | 0 | \((-\infty, \infty)\) | |
| categorical_encoding | character | AUTO | AUTO, Enum, OneHotInternal, OneHotExplicit, Binary, Eigen, LabelEncoder, SortByResponse, EnumLimited | - |
| checkpoint | untyped | NULL | - | |
| diagnostics | logical | TRUE | TRUE, FALSE | - |
| distribution | character | AUTO | AUTO, gaussian, poisson, gamma, tweedie, laplace, huber, quantile | - |
| elastic_averaging | logical | FALSE | TRUE, FALSE | - |
| elastic_averaging_moving_rate | numeric | 0.9 | \((-\infty, \infty)\) | |
| elastic_averaging_regularization | numeric | 0.001 | \((-\infty, \infty)\) | |
| epochs | numeric | 10 | \([1, \infty)\) | |
| epsilon | numeric | 1e-08 | \([1e-10, 1e-04]\) | |
| export_checkpoints_dir | untyped | NULL | - | |
| export_weights_and_biases | logical | FALSE | TRUE, FALSE | - |
| fast_mode | logical | TRUE | TRUE, FALSE | - |
| force_load_balance | logical | TRUE | TRUE, FALSE | - |
| hidden | untyped | c(200L, 200L) | - | |
| hidden_dropout_ratios | numeric | 0.5 | \((-\infty, \infty)\) | |
| huber_alpha | numeric | 0.9 | \([0, 1]\) | |
| ignore_const_cols | logical | TRUE | TRUE, FALSE | - |
| initial_weight_distribution | character | UniformAdaptive | UniformAdaptive, Uniform, Normal | - |
| initial_weight_scale | numeric | 1 | \((-\infty, \infty)\) | |
| input_dropout_ratio | numeric | 0 | \((-\infty, \infty)\) | |
| l1 | numeric | 0 | \((-\infty, \infty)\) | |
| l2 | numeric | 0 | \((-\infty, \infty)\) | |
| loss | character | Automatic | Automatic, Quantile, Quadratic, Absolute, Huber | - |
| max_categorical_features | integer | 2147483647 | \([1, \infty)\) | |
| max_runtime_secs | numeric | 0 | \([0, \infty)\) | |
| max_w2 | numeric | 3.402823e+38 | \((-\infty, \infty)\) | |
| mini_batch_size | integer | 1 | \((-\infty, \infty)\) | |
| missing_values_handling | character | MeanImputation | MeanImputation, Skip | - |
| momentum_ramp | numeric | 1e+06 | \((-\infty, \infty)\) | |
| momentum_stable | numeric | 0 | \((-\infty, \infty)\) | |
| momentum_start | numeric | 0 | \((-\infty, \infty)\) | |
| nesterov_accelerated_gradient | logical | TRUE | TRUE, FALSE | - |
| overwrite_with_best_model | logical | TRUE | TRUE, FALSE | - |
| pretrained_autoencoder | untyped | NULL | - | |
| quantile_alpha | numeric | 0.5 | \([0, 1]\) | |
| quiet_mode | logical | FALSE | TRUE, FALSE | - |
| rate | numeric | 0.005 | \([0, 1]\) | |
| rate_annealing | numeric | 1e-06 | \([0, \infty)\) | |
| rate_decay | numeric | 1 | \([0, \infty)\) | |
| regression_stop | numeric | 1e-06 | \([-1, \infty)\) | |
| replicate_training_data | logical | TRUE | TRUE, FALSE | - |
| reproducible | logical | FALSE | TRUE, FALSE | - |
| rho | numeric | 0.99 | \([0, \infty)\) | |
| score_duty_cycle | numeric | 0.1 | \((-\infty, \infty)\) | |
| score_each_iteration | logical | FALSE | TRUE, FALSE | - |
| score_interval | numeric | 5 | \((-\infty, \infty)\) | |
| score_training_samples | integer | 10000 | \((-\infty, \infty)\) | |
| score_validation_samples | integer | 0 | \((-\infty, \infty)\) | |
| seed | integer | -1 | \((-\infty, \infty)\) | |
| shuffle_training_data | logical | FALSE | TRUE, FALSE | - |
| single_node_mode | logical | FALSE | TRUE, FALSE | - |
| sparse | logical | FALSE | TRUE, FALSE | - |
| sparsity_beta | numeric | 0 | \((-\infty, \infty)\) | |
| standardize | logical | TRUE | TRUE, FALSE | - |
| stopping_metric | character | AUTO | AUTO, deviance, MSE, RMSE, MAE, RMSLE | - |
| stopping_rounds | integer | 5 | \([0, \infty)\) | |
| stopping_tolerance | numeric | 0 | \([0, \infty)\) | |
| target_ratio_comm_to_comp | numeric | 0.05 | \((-\infty, \infty)\) | |
| train_samples_per_iteration | integer | -2 | \([-2, \infty)\) | |
| tweedie_power | numeric | 1.5 | \([1, 2]\) | |
| verbose | logical | FALSE | TRUE, FALSE | - |
References
Fryda T, LeDell E, Gill N, Aiello S, Fu A, Candel A, Click C, Kraljevic T, Nykodym T, Aboyoun P, Kurka M, Malohlava M, Poirier S, Wong W (2025). h2o: R Interface for the 'H2O' Scalable Machine Learning Platform. R package version 3.46.0.9, https://github.com/h2oai/h2o-3.
See also
as.data.table(mlr_learners)for a table of available Learners in the running session (depending on the loaded packages).Chapter in the mlr3book: https://mlr3book.mlr-org.com/basics.html#learners
mlr3learners for a selection of recommended learners.
mlr3cluster for unsupervised clustering learners.
mlr3pipelines to combine learners with pre- and postprocessing steps.
mlr3tuning for tuning of hyperparameters, mlr3tuningspaces for established default tuning spaces.
Super classes
mlr3::Learner -> mlr3::LearnerRegr -> LearnerRegrH2ODeeplearning
Methods
Inherited methods
mlr3::Learner$base_learner()mlr3::Learner$configure()mlr3::Learner$encapsulate()mlr3::Learner$format()mlr3::Learner$help()mlr3::Learner$predict()mlr3::Learner$predict_newdata()mlr3::Learner$print()mlr3::Learner$reset()mlr3::Learner$selected_features()mlr3::Learner$train()mlr3::LearnerRegr$predict_newdata_fast()
Examples
# Define the Learner
learner = lrn("regr.h2o.deeplearning")
print(learner)
#>
#> ── <LearnerRegrH2ODeeplearning> (regr.h2o.deeplearning): H2O Deep Learning ─────
#> • Model: -
#> • Parameters: list()
#> • Packages: mlr3, mlr3extralearners, and h2o
#> • Predict Types: [response]
#> • Feature Types: integer, numeric, and factor
#> • Encapsulation: none (fallback: -)
#> • Properties: missings and weights
#> • Other settings: use_weights = 'use'
# Define a Task
task = tsk("mtcars")
# Create train and test set
ids = partition(task)
# Train the learner on the training ids
learner$train(task, row_ids = ids$train)
print(learner$model)
#> Model Details:
#> ==============
#>
#> H2ORegressionModel: deeplearning
#> Model ID: DeepLearning_model_R_1774260318250_109
#> Status of Neuron Layers: predicting mpg, regression, gaussian distribution, Quadratic loss, 42,601 weights/biases, 508.1 KB, 210 training samples, mini-batch size 1
#> layer units type dropout l1 l2 mean_rate rate_rms momentum
#> 1 1 10 Input 0.00 % NA NA NA NA NA
#> 2 2 200 Rectifier 0.00 % 0.000000 0.000000 0.008443 0.007981 0.000000
#> 3 3 200 Rectifier 0.00 % 0.000000 0.000000 0.035303 0.126747 0.000000
#> 4 4 1 Linear NA 0.000000 0.000000 0.015422 0.120246 0.000000
#> mean_weight weight_rms mean_bias bias_rms
#> 1 NA NA NA NA
#> 2 0.004180 0.100173 0.498222 0.002495
#> 3 -0.000138 0.069275 0.999581 0.001205
#> 4 -0.004985 0.101167 0.000282 0.000000
#>
#>
#> H2ORegressionMetrics: deeplearning
#> ** Reported on training data. **
#> ** Metrics reported on full training frame **
#>
#> MSE: 18.96121
#> RMSE: 4.354447
#> MAE: 3.573819
#> RMSLE: 0.2217358
#> Mean Residual Deviance : 18.96121
#>
#>
#>
#>
# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)
# Score the predictions
predictions$score()
#> regr.mse
#> 10.07329