FastForestOptionsBase Class
Definition
Important
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Base class for fast forest trainer options.
public abstract class FastForestOptionsBase : Microsoft.ML.Trainers.FastTree.TreeOptionstype FastForestOptionsBase = class
inherit TreeOptionsPublic MustInherit Class FastForestOptionsBase
Inherits TreeOptions- Inheritance
- Derived
Fields
| AllowEmptyTrees |
When a root split is impossible, allow training to proceed. (Inherited from TreeOptions) |
| BaggingExampleFraction |
Percentage of training examples used in each bag. Default is 0.7 (70%). (Inherited from TreeOptions) |
| BaggingSize |
Number of trees in each bag (0 for disabling bagging). (Inherited from TreeOptions) |
| Bias |
Bias for calculating gradient for each feature bin for a categorical feature. (Inherited from TreeOptions) |
| Bundling |
Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle. (Inherited from TreeOptions) |
| CategoricalSplit |
Whether to do split based on multiple categorical feature values. (Inherited from TreeOptions) |
| CompressEnsemble |
Compress the tree Ensemble. (Inherited from TreeOptions) |
| DiskTranspose |
Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose. (Inherited from TreeOptions) |
| EntropyCoefficient |
The entropy (regularization) coefficient between 0 and 1. (Inherited from TreeOptions) |
| ExampleWeightColumnName |
Column to use for example weight. (Inherited from TrainerInputBaseWithWeight) |
| ExecutionTime |
Print execution time breakdown to ML.NET channel. (Inherited from TreeOptions) |
| FeatureColumnName |
Column to use for features. (Inherited from TrainerInputBase) |
| FeatureFirstUsePenalty |
The feature first use penalty coefficient. (Inherited from TreeOptions) |
| FeatureFlocks |
Whether to collectivize features during dataset preparation to speed up training. (Inherited from TreeOptions) |
| FeatureFraction |
The fraction of features (chosen randomly) to use on each iteration. Use 0.9 if only 90% of features is needed. Lower numbers help reduce over-fitting. (Inherited from TreeOptions) |
| FeatureFractionPerSplit |
The fraction of features (chosen randomly) to use on each split. If it's value is 0.9, 90% of all features would be dropped in expectation. (Inherited from TreeOptions) |
| FeatureReusePenalty |
The feature re-use penalty (regularization) coefficient. (Inherited from TreeOptions) |
| FeatureSelectionSeed |
The seed of the active feature selection. (Inherited from TreeOptions) |
| GainConfidenceLevel |
Tree fitting gain confidence requirement. Only consider a gain if its likelihood versus a random choice gain is above this value. (Inherited from TreeOptions) |
| HistogramPoolSize |
The number of histograms in the pool (between 2 and numLeaves). (Inherited from TreeOptions) |
| LabelColumnName |
Column to use for labels. (Inherited from TrainerInputBaseWithLabel) |
| MaximumBinCountPerFeature |
Maximum number of distinct values (bins) per feature. (Inherited from TreeOptions) |
| MaximumCategoricalGroupCountPerNode |
Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features. (Inherited from TreeOptions) |
| MaximumCategoricalSplitPointCount |
Maximum categorical split points to consider when splitting on a categorical feature. (Inherited from TreeOptions) |
| MemoryStatistics |
Print memory statistics to ML.NET channel. (Inherited from TreeOptions) |
| MinimumExampleCountPerLeaf |
The minimal number of data points required to form a new tree leaf. (Inherited from TreeOptions) |
| MinimumExampleFractionForCategoricalSplit |
Minimum categorical example percentage in a bin to consider for a split. Default is 0.1% of all training examples. (Inherited from TreeOptions) |
| MinimumExamplesForCategoricalSplit |
Minimum categorical example count in a bin to consider for a split. (Inherited from TreeOptions) |
| NumberOfLeaves |
The max number of leaves in each regression tree. (Inherited from TreeOptions) |
| NumberOfQuantileSamples |
The number of data points to be sampled from each leaf to find the distribution of labels. |
| NumberOfThreads |
The number of threads to use. (Inherited from TreeOptions) |
| NumberOfTrees |
Total number of decision trees to create in the ensemble. (Inherited from TreeOptions) |
| RowGroupColumnName |
Column to use for example groupId. (Inherited from TrainerInputBaseWithGroupId) |
| Seed |
The seed of the random number generator. (Inherited from TreeOptions) |
| Smoothing |
Smoothing parameter for tree regularization. (Inherited from TreeOptions) |
| SoftmaxTemperature |
The temperature of the randomized softmax distribution for choosing the feature. (Inherited from TreeOptions) |
| SparsifyThreshold |
Sparsity level needed to use sparse feature representation. (Inherited from TreeOptions) |
| TestFrequency |
Calculate metric values for train/valid/test every k rounds. (Inherited from TreeOptions) |