TableFixedParameters Class
Definition
Important
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Fixed training parameters that won't be swept over during AutoML Table training.
public class TableFixedParameterstype TableFixedParameters = classPublic Class TableFixedParameters- Inheritance
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TableFixedParameters
Constructors
| TableFixedParameters() |
Initializes a new instance of TableFixedParameters. |
Properties
| Booster |
Specify the boosting type, e.g gbdt for XGBoost. |
| BoostingType |
Specify the boosting type, e.g gbdt for LightGBM. |
| GrowPolicy |
Specify the grow policy, which controls the way new nodes are added to the tree. |
| LearningRate |
The learning rate for the training procedure. |
| MaxBin |
Specify the Maximum number of discrete bins to bucket continuous features . |
| MaxDepth |
Specify the max depth to limit the tree depth explicitly. |
| MaxLeaves |
Specify the max leaves to limit the tree leaves explicitly. |
| MinDataInLeaf |
The minimum number of data per leaf. |
| MinSplitGain |
Minimum loss reduction required to make a further partition on a leaf node of the tree. |
| ModelName |
The name of the model to train. |
| NEstimators |
Specify the number of trees (or rounds) in an model. |
| NumLeaves |
Specify the number of leaves. |
| PreprocessorName |
The name of the preprocessor to use. |
| RegAlpha |
L1 regularization term on weights. |
| RegLambda |
L2 regularization term on weights. |
| Subsample |
Subsample ratio of the training instance. |
| SubsampleFreq |
Frequency of subsample. |
| TreeMethod |
Specify the tree method. |
| WithMean |
If true, center before scaling the data with StandardScalar. |
| WithStd |
If true, scaling the data with Unit Variance with StandardScalar. |
