public class SharedTreeParametersV3 extends ModelParametersSchemaV3
| Modifier and Type | Field and Description |
|---|---|
boolean |
balanceClasses
Balance training data class counts via over/under-sampling (for imbalanced data).
|
boolean |
buildTreeOneNode
Run on one node only; no network overhead but fewer cpus used.
|
float[] |
classSamplingFactors
Desired over/under-sampling ratios per class (in lexicographic order).
|
double |
colSampleRateChangePerLevel
Relative change of the column sampling rate for every level (from 0.0 to 2.0)
|
double |
colSampleRatePerTree
Column sample rate per tree (from 0.0 to 1.0)
|
TreeSharedTreeModelSharedTreeParametersHistogramType |
histogramType
What type of histogram to use for finding optimal split points
|
float |
maxAfterBalanceSize
Maximum relative size of the training data after balancing class counts (can be less than 1.0).
|
int |
maxConfusionMatrixSize
[Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs
|
int |
maxDepth
Maximum tree depth.
|
int |
maxHitRatioK
Max.
|
double |
minRows
Fewest allowed (weighted) observations in a leaf.
|
double |
minSplitImprovement
Minimum relative improvement in squared error reduction for a split to happen
|
int |
nbins
For numerical columns (real/int), build a histogram of (at least) this many bins, then split at the best point
|
int |
nbinsCats
For categorical columns (factors), build a histogram of this many bins, then split at the best point.
|
int |
nbinsTopLevel
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 |
ntrees
Number of trees.
|
double |
r2Stopping
r2_stopping is no longer supported and will be ignored if set - please use stopping_rounds, stopping_metric and
stopping_tolerance instead.
|
double |
sampleRate
Row sample rate per tree (from 0.0 to 1.0)
|
double[] |
sampleRatePerClass
Row sample rate per tree per class (from 0.0 to 1.0)
|
int |
scoreTreeInterval
Score the model after every so many trees.
|
long |
seed
Seed for pseudo random number generator (if applicable)
|
categoricalEncoding, checkpoint, distribution, foldAssignment, foldColumn, huberAlpha, ignoreConstCols, ignoredColumns, keepCrossValidationFoldAssignment, keepCrossValidationPredictions, maxRuntimeSecs, modelId, nfolds, offsetColumn, parallelizeCrossValidation, quantileAlpha, responseColumn, scoreEachIteration, stoppingMetric, stoppingRounds, stoppingTolerance, trainingFrame, tweediePower, validationFrame, weightsColumn| Constructor and Description |
|---|
SharedTreeParametersV3()
Public constructor
|
| Modifier and Type | Method and Description |
|---|---|
java.lang.String |
toString()
Return the contents of this object as a JSON String.
|
public boolean balanceClasses
public float[] classSamplingFactors
public float maxAfterBalanceSize
public int maxConfusionMatrixSize
public int maxHitRatioK
public int ntrees
public int maxDepth
public double minRows
public int nbins
public int nbinsTopLevel
public int nbinsCats
public double r2Stopping
public long seed
public boolean buildTreeOneNode
public double sampleRate
public double[] sampleRatePerClass
public double colSampleRatePerTree
public double colSampleRateChangePerLevel
public int scoreTreeInterval
public double minSplitImprovement
public TreeSharedTreeModelSharedTreeParametersHistogramType histogramType
public java.lang.String toString()
toString in class ModelParametersSchemaV3