DRFParametersV3 (h2o-java-rest-bindings version 3.8.0.5 API)

java.lang.Object
- water.bindings.pojos.Schema
- - water.bindings.pojos.ModelParametersSchema
  - - water.bindings.pojos.SharedTreeParametersV3
    - - water.bindings.pojos.DRFParametersV3

public class DRFParametersV3
extends SharedTreeParametersV3

Field Summary

Fields
Modifier and Type	Field and Description
`boolean`	`balance_classes` Balance training data class counts via over/under-sampling (for imbalanced data).
`boolean`	`binomial_double_trees` For binary classification: Build 2x as many trees (one per class) - can lead to higher accuracy.
`boolean`	`build_tree_one_node` Run on one node only; no network overhead but fewer cpus used.
`java.lang.String`	`checkpoint` Model checkpoint to resume training with
`float[]`	`class_sampling_factors` Desired over/under-sampling ratios per class (in lexicographic order).
`float`	`col_sample_rate_per_tree` Column sample rate per tree (from 0.0 to 1.0)
`FoldAssignmentScheme`	`fold_assignment` Cross-validation fold assignment scheme, if fold_column is not specified
`ColSpecifierV3`	`fold_column` Column with cross-validation fold index assignment per observation
`boolean`	`ignore_const_cols` Ignore constant columns
`java.lang.String[]`	`ignored_columns` Ignored columns
`boolean`	`keep_cross_validation_predictions` Keep cross-validation model predictions
`float`	`max_after_balance_size` Maximum relative size of the training data after balancing class counts (can be less than 1.0).
`int`	`max_confusion_matrix_size` Maximum size (# classes) for confusion matrices to be printed in the Logs
`int`	`max_depth` Maximum tree depth.
`int`	`max_hit_ratio_k` Max.
`double`	`max_runtime_secs` Maximum allowed runtime in seconds for model training.
`double`	`min_rows` Fewest allowed (weighted) observations in a leaf (in R called 'nodesize').
`java.lang.String`	`model_id` Destination id for this model; auto-generated if not specified
`int`	`mtries` Number of variables randomly sampled as candidates at each split.
`int`	`nbins` For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point
`int`	`nbins_cats` For categorical columns (factors), build a histogram of this many bins, then split at the best point.
`int`	`nbins_top_level` For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level
`int`	`nfolds` Number of folds for N-fold cross-validation
`int`	`ntrees` Number of trees.
`ColSpecifierV3`	`offset_column` Offset column
`boolean`	`parallelize_cross_validation` Allow parallel training of cross-validation models
`double`	`r2_stopping` Stop making trees when the R^2 metric equals or exceeds this
`ColSpecifierV3`	`response_column` Response column
`float`	`sample_rate` Row sample rate (from 0.0 to 1.0)
`boolean`	`score_each_iteration` Whether to score during each iteration of model training
`int`	`score_tree_interval` Score the model after every so many trees.
`long`	`seed` Seed for pseudo random number generator (if applicable)
`StoppingMetric`	`stopping_metric` Metric to use for early stopping (AUTO: logloss for classification, deviance for regression)
`int`	`stopping_rounds` Early stopping based on convergence of stopping_metric.
`double`	`stopping_tolerance` Relative tolerance for metric-based stopping criterion Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
`java.lang.String`	`training_frame` Training frame
`java.lang.String`	`validation_frame` Validation frame
`ColSpecifierV3`	`weights_column` Column with observation weights

Constructor Summary

Constructors
Constructor and Description

DRFParametersV3()

Constructors
Constructor and Description
`DRFParametersV3()`

Method Summary
- Methods inherited from class java.lang.Object
  clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

- Field Detail
  - mtries
```
public int mtries
```
    Number of variables randomly sampled as candidates at each split. If set to -1, defaults to sqrt{p} for classification and p/3 for regression (where p is the # of predictors
  - binomial_double_trees
```
public boolean binomial_double_trees
```
    For binary classification: Build 2x as many trees (one per class) - can lead to higher accuracy.
  - balance_classes
```
public boolean balance_classes
```
    Balance training data class counts via over/under-sampling (for imbalanced data).
  - class_sampling_factors
```
public float[] class_sampling_factors
```
    Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.
  - max_after_balance_size
```
public float max_after_balance_size
```
    Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.
  - max_confusion_matrix_size
```
public int max_confusion_matrix_size
```
    Maximum size (# classes) for confusion matrices to be printed in the Logs
  - max_hit_ratio_k
```
public int max_hit_ratio_k
```
    Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)
  - ntrees
```
public int ntrees
```
    Number of trees.
  - max_depth
```
public int max_depth
```
    Maximum tree depth.
  - min_rows
```
public double min_rows
```
    Fewest allowed (weighted) observations in a leaf (in R called 'nodesize').
  - nbins
```
public int nbins
```
    For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point
  - nbins_top_level
```
public int nbins_top_level
```
    For numerical columns (real/int), build a histogram of (at most) this many bins at the root level, then decrease by factor of two per level
  - nbins_cats
```
public int nbins_cats
```
    For categorical columns (factors), build a histogram of this many bins, then split at the best point. Higher values can lead to more overfitting.
  - r2_stopping
```
public double r2_stopping
```
    Stop making trees when the R^2 metric equals or exceeds this
  - seed
```
public long seed
```
    Seed for pseudo random number generator (if applicable)
  - build_tree_one_node
```
public boolean build_tree_one_node
```
    Run on one node only; no network overhead but fewer cpus used. Suitable for small datasets.
  - sample_rate
```
public float sample_rate
```
    Row sample rate (from 0.0 to 1.0)
  - col_sample_rate_per_tree
```
public float col_sample_rate_per_tree
```
    Column sample rate per tree (from 0.0 to 1.0)
  - score_tree_interval
```
public int score_tree_interval
```
    Score the model after every so many trees. Disabled if set to 0.
  - model_id
```
public java.lang.String model_id
```
    Destination id for this model; auto-generated if not specified
  - training_frame
```
public java.lang.String training_frame
```
    Training frame
  - validation_frame
```
public java.lang.String validation_frame
```
    Validation frame
  - nfolds
```
public int nfolds
```
    Number of folds for N-fold cross-validation
  - keep_cross_validation_predictions
```
public boolean keep_cross_validation_predictions
```
    Keep cross-validation model predictions
  - parallelize_cross_validation
```
public boolean parallelize_cross_validation
```
    Allow parallel training of cross-validation models
  - response_column
```
public ColSpecifierV3 response_column
```
    Response column
  - weights_column
```
public ColSpecifierV3 weights_column
```
    Column with observation weights
  - offset_column
```
public ColSpecifierV3 offset_column
```
    Offset column
  - fold_column
```
public ColSpecifierV3 fold_column
```
    Column with cross-validation fold index assignment per observation
  - fold_assignment
```
public FoldAssignmentScheme fold_assignment
```
    Cross-validation fold assignment scheme, if fold_column is not specified
  - ignored_columns
```
public java.lang.String[] ignored_columns
```
    Ignored columns
  - ignore_const_cols
```
public boolean ignore_const_cols
```
    Ignore constant columns
  - score_each_iteration
```
public boolean score_each_iteration
```
    Whether to score during each iteration of model training
  - checkpoint
```
public java.lang.String checkpoint
```
    Model checkpoint to resume training with
  - stopping_rounds
```
public int stopping_rounds
```
    Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable)
  - max_runtime_secs
```
public double max_runtime_secs
```
    Maximum allowed runtime in seconds for model training. Use 0 to disable.
  - stopping_metric
```
public StoppingMetric stopping_metric
```
    Metric to use for early stopping (AUTO: logloss for classification, deviance for regression)
  - stopping_tolerance
```
public double stopping_tolerance
```
    Relative tolerance for metric-based stopping criterion Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much)
- Constructor Detail
  - DRFParametersV3
```
public DRFParametersV3()
```

Class DRFParametersV3

Field Summary

Constructor Summary

Method Summary

Methods inherited from class java.lang.Object

Field Detail

mtries

binomial_double_trees

balance_classes

class_sampling_factors

max_after_balance_size

max_confusion_matrix_size

max_hit_ratio_k

ntrees

max_depth

min_rows

nbins

nbins_top_level

nbins_cats

r2_stopping

seed

build_tree_one_node

sample_rate

col_sample_rate_per_tree

score_tree_interval

model_id

training_frame

validation_frame

nfolds

keep_cross_validation_predictions

parallelize_cross_validation

response_column

weights_column

offset_column

fold_column

fold_assignment

ignored_columns

ignore_const_cols

score_each_iteration

checkpoint

stopping_rounds

max_runtime_secs

stopping_metric

stopping_tolerance

Constructor Detail

DRFParametersV3