- x
(Optional). A vector containing the names or indices of the predictor variables to use in building the model.
If x is missing, then all columns except y are used. Training frame is used only to compute ensemble training metrics.

- y
The name or column index of the response variable in the data.
The response must be either a numeric or a categorical/factor variable.
If the response is numeric, then a regression model will be trained, otherwise it will train a classification model.

- training_frame
Id of the training data frame.

- model_id
Destination id for this model; auto-generated if not specified.

- validation_frame
Id of the validation data frame.

- blending_frame
Frame used to compute the predictions that serve as the training frame for the metalearner (triggers blending
mode if provided)

- base_models
List of models or grids (or their ids) to ensemble/stack together. Grids are expanded to individual models. If
not using blending frame, then models must have been cross-validated using nfolds > 1, and folds must be
identical across models.

- metalearner_algorithm
Type of algorithm to use as the metalearner. Options include 'AUTO' (GLM with non negative weights; if
validation_frame is present, a lambda search is performed), 'deeplearning' (Deep Learning with default
parameters), 'drf' (Random Forest with default parameters), 'gbm' (GBM with default parameters), 'glm' (GLM
with default parameters), 'naivebayes' (NaiveBayes with default parameters), or 'xgboost' (if available,
XGBoost with default parameters). Must be one of: "AUTO", "deeplearning", "drf", "gbm", "glm", "naivebayes",
"xgboost". Defaults to AUTO.

- metalearner_nfolds
Number of folds for K-fold cross-validation of the metalearner algorithm (0 to disable or >= 2). Defaults to
0.

- metalearner_fold_assignment
Cross-validation fold assignment scheme for metalearner cross-validation. Defaults to AUTO (which is
currently set to Random). The 'Stratified' option will stratify the folds based on the response variable, for
classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified".

- metalearner_fold_column
Column with cross-validation fold index assignment per observation for cross-validation of the metalearner.

- metalearner_params
Parameters for metalearner algorithm

- metalearner_transform
Transformation used for the level one frame. Must be one of: "NONE", "Logit". Defaults to NONE.

- max_runtime_secs
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0.

- weights_column
Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from
the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative
weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the
data frame. This is typically the number of times a row is repeated, but non-integer values are supported as
well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If
you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get
an accurate prediction, remove all rows with weight == 0.

- offset_column
Offset column. This will be added to the combination of columns before applying the link function.

- custom_metric_func
Reference to custom evaluation function, format: `language:keyName=funcName`

- seed
Seed for random numbers; passed through to the metalearner algorithm. Defaults to -1 (time-based random number).

- score_training_samples
Specify the number of training set samples for scoring. The value must be >= 0. To use all training samples,
enter 0. Defaults to 10000.

- keep_levelone_frame
`Logical`

. Keep level one frame used for metalearner training. Defaults to FALSE.

- export_checkpoints_dir
Automatically export generated models to this directory.

- auc_type
Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO",
"WEIGHTED_OVO". Defaults to AUTO.

- gainslift_bins
Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. Defaults to -1.