TD_​XGBoost

The TD_XGBoost is an implementation of gradient boosted decision tree designed for speed and performance. The TD_XGBoost function supports both regression and classification predictive modeling problems. The model created by it is used in the TD_XGBoostPredict function for making predictions.

Options

ColumnSampling

Specify the fraction of features to sample during boosting. The sample_fraction is a DOUBLE PRECISION value in the range (0, 1].

CoverageFactor

Specify the level of coverage for the dataset while boosting trees (in percentage, e.g., 1.25 = 125% coverage). CoverageFactor can only be used if NumBoostedTrees is not supplied. When NumBoostedTrees is specified, coverage depends on the value of NumBoostedTrees. If NumBoostedTrees is not specified, NumBoostedTrees is chosen to achieve this level of coverage.

InputColumns

Specify the names of the input table columns that need to be used for training the model (predictors, features or independent variables).

IterNum

Specify the number of iterations (rounds) to boost the weak classifiers. The iterations must be an INTEGER in the range [1, 100000].

MaxDepth

Specify a decision tree stopping criterion. If the tree reaches a depth past this value, the algorithm stops looking for splits. Decision trees can grow to (2^(max_depth+1)-1) nodes. This stopping criterion has the greatest effect on the performance of the function.

MinImpurity

Specify the minimum impurity at which the tree stops splitting further down. For regression, a criteria of squared error is used whereas for classification, gini impurity is used.

MinNodeSize

Specify a decision tree stopping criterion; the minimum size of any node within each decision tree.

ModelType

Specify whether the analysis is a regression (continuous response variable) or a multiple-class classification (predicting result from the number of classes).

NumBoostedTrees

Specify the number of parallels boosted trees.Each boosted tree operates on a sample of data that fits in an AMP's memory. By default, NumBoostedTrees is chosen equal to the number of AMPs with data.

RegularizationLambda

Specify the L2 regularization that the loss function uses while boosting trees. The lambda is a DOUBLE PRECISION value in the range [0, 100000]. The higher the lambda, the stronger the regularization effect. The value 0 specifies no regularization.

ResponseColumn

Specify the name of the column that contains the class label for classification or target value (dependent variable) for regression.

Seed

Specify an integer value to use in determining the random seed for column sampling. By default, seed is 1.

ShrinkageFactor

Specify the learning rate (weight) of a learned tree in each boosting step. After each boosting step, the algorithm multiplies the learner by shrinkage to make the boosting process more conservative. The shrinkage is a DOUBLE PRECISION value in the range (0, 1]. The value 1 specifies no shrinkage.

Output Schema

Output Schema, if Volatile is true then use user login as the schema.

Output Table

Output Table

VAL Location

VAL Location

Volatile

Specifies whether the table should be a VOLATILE table. If true, then the table is automatically deleted, otherwise it is users responsibility to remove or clean it up for space.

TreeSize

Specify the number of rows that each tree uses as its input dataset.

Nodes

• Integer Configuration6 ×
• Double Configuration5 ×
• String Configuration3 ×
• Column Filter Configuration2 ×
• Python Script2 ×
• Table Row to Variable2 ×
• Boolean Configuration1 ×
• Component Input1 ×
• Component Output1 ×
• DB Data Spec Extractor1 ×
• DB SQL Executor (Labs)1 ×
• DB Table Remover (Labs)1 ×
• DB Table Selector (Labs)1 ×
• Merge Variables1 ×
• Single Selection Configuration1 ×
• Transpose1 ×

Extensions

• FeatureKNIME Base nodes
• FeatureKNIME Database
• FeatureKNIME Quick Forms

Links

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