
DRF
===

  

Supported HTTP methods and descriptions
---------------------------------------


URL
---

  http://<h2oHost>:<h2oApiPort>/DRF.json

Input parameters
----------------


*  **classification**, a boolean, <i>expert</i>

   Do classification or regression.  Since version 1

*  **validation**, a Frame, <i>expert</i>

   Validation frame.  Since version 1

*  **n_folds**, a int, <i>expert</i>

   Number of folds for cross-validation (if no validation data is specified).  Since version 1

*  **holdout_fraction**, a float, <i>expert</i>

   Fraction of training data (from end) to hold out for validation (if no validation data is specified).  Since version 1

*  **keep_cross_validation_splits**, a boolean, <i>expert</i>

   Keep cross-validation dataset splits.  Since version 1

*  **ntrees**, a int, <i>critical</i>

   Number of trees. Grid Search, comma sep values:50,100,150,200.  Since version 1

*  **max_depth**, a int, <i>critical</i>

   Maximum tree depth. Grid Search, comma sep values:5,7.  Since version 1

*  **min_rows**, a int, <i>secondary</i>

   Fewest allowed observations in a leaf (in R called 'nodesize'). Grid Search, comma sep values.  Since version 1

*  **nbins**, a int, <i>secondary</i>

   Build a histogram of this many bins, then split at the best point.  Since version 1

*  **score_each_iteration**, a boolean, <i>expert</i>

   Perform scoring after each iteration (can be slow).  Since version 1

*  **importance**, a boolean, <i>expert</i>

   Compute variable importance (true/false)..  Since version 1

*  **balance_classes**, a boolean, <i>expert</i>

   Balance training data class counts via over/under-sampling (for imbalanced data).  Since version 1

*  **class_sampling_factors**, a float[], <i>secondary</i>

   Desired over/under-sampling ratios per class (lexicographic order)..  Since version 1

*  **max_after_balance_size**, a float, <i>expert</i>

   Maximum relative size of the training data after balancing class counts (can be less than 1.0).  Since version 1

*  **checkpoint**, a Key, <i>expert</i>

   Model checkpoint to start building a new model from.  Since version 1

*  **overwrite_checkpoint**, a boolean, <i>expert</i>

   Overwrite checkpoint.  Since version 1

*  **mtries**, a int, <i>expert</i>

   Columns to randomly select at each level, or -1 for sqrt(#cols).  Since version 1

*  **sample_rate**, a float, <i>secondary</i>

   Sample rate, from 0. to 1.0.  Since version 1

*  **seed**, a long, <i>expert</i>

   Seed for the random number generator (autogenerated).  Since version 1

*  **do_grpsplit**, a boolean, <i>expert</i>

   Check non-contiguous group splits for categorical predictors.  Since version 1

*  **build_tree_one_node**, a boolean, <i>secondary</i>

   Run on one node only; no network overhead but fewer cpus used.  Suitable for small datasets..  Since version 1



Output JSON elements
--------------------


*  **xval_models**, a Key[]

   Cross-validation models.  Since version 1, expert

*  **_distribution**, a long[]

   Class distribution.  Since version 1, expert

*  **_mtry**, a int

   Computed number of split features.  Since version 1, expert

*  **_seed**, a long

   Autogenerated seed.  Since version 1, expert



HTTP response codes
-------------------

  200 OK
  Success and error responses are identical.
