TD_SVM Syntax Elements - Teradata Vantage

Teradata® VantageCloud Lake
Deployment
VantageCloud
Edition
Lake
Product
Teradata Vantage
Published
January 2023
Language
English (United States)
Last Update
2024-04-03
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InputColumns
Specify the names of the input table columns that need to be used for training the model (predictors, features, or independent variables).
ResponseColumn
Specify the name of the column that contains the class label for classification or target value (dependent variable) for regression.
ModelType
[Optional] Specify the type of the analysis. Acceptable values are Regression, Classification.
Default value: Classification
MaxIterNum
[Optional] Specify the maximum number of iterations (minibatches) over the training data batches. The value is a positive integer less than 10,000,000.
Default value: 300
Epsilon
[Optional] Specify the epsilon threshold for Regression (the value of epsilon for epsilon_insensitive loss). Any difference between the current prediction and the correct label is ignored within this threshold. The value is numeric.
Default: 0.1
BatchSize
[Optional] Specify the number of observations (training samples) processed in a single minibatch per AMP. A value of 0 or higher than the number of rows on an AMP processes all rows on the AMP, such that the entire dataset is processed in a single iteration, and the algorithm becomes Gradient Descent. The value is a non-negative integer.
Default value: 10
RegularizationLambda
[Optional] Specify the amount of regularization to be added. The higher the value, stronger the regularization. It is also used to compute learning rate when learning rate is set to optimal. It must be a non-negative float value. A value of 0 means no regularization.
Default: 0.02
Alpha
[Optional] Specify the Elasticnet parameter for penalty computation. It is only effective when RegularizationLambda is greater than 0. The value represents the contribution ratio of L1 in the penalty. A value of 1.0 indicates L1 (LASSO) only, a value of 0 indicates L2 (Ridge) only, and a value between is a combination of L1 and L2. The value is a float value between 0 and 1.
Default: 0.15
IterNumNoChange
[Optional] Specify the number of iterations (minibatches) with no improvement in loss, including the tolerance to stop training. A value of 0 indicates no early stopping and the algorithm continues until MaxIterNum iterations are reached. The value is a non-negative integer.
Default: 50
Tolerance
[Optional] Specify the stopping criteria in terms of loss function improvement. Applicable when IterNumNoChange is greater than 0. The value is a positive integer.
Default: 0.001
Intercept
[Optional] Specify whether to estimate intercept based on whether data is already centered.
Default: True
ClassWeights
[Optional] Specify weights associated with classes. Only applicable for Classification. The format is 0:weight, 1:weight. For example, 0:1.0,1:0.5 gives twice the weight to each observation in class 0. If the weight of a class is omitted, it is assumed to be 1.0.
Default: 0:1.0, 1:1.0
LearningRate
[Optional] Specify the learning rate algorithm. Learning rates are:
  • Constant
  • InvTime
  • Optimal
  • Adaptive
Default: invtime for Regression, optimal for Classification
InitialEta
[Optional] Specify the initial value of eta for learning rate. For LearningRate set to constant, this value is the learning rate for all iterations.
Default: 0.05
DecayRate
[Optional] Specify the decay rate for learning rate. Only applicable for learning rates invtime and adaptive.
Default: 0.25
DecaySteps
[Optional] Specify the number of iterations without decay for the adaptive learning rate. The learning rate changes by decay rate after this many iterations.
Default: 5
Momentum
[Optional] Specify the value to use for momentum learning rate optimizer. A larger value indicates higher momentum contribution. A value of 0 means momentum optimizer is disabled. For a good momentum contribution, a value between 0.6-0.95 is recommended. The value is a non-negative float between 0 and 1.
Default: 0
Nesterov
[Optional] Specify whether to apply Nesterov optimization to Momentum optimizer. Only applicable when Momentum is greater than 0.
Default: False
LocalSGDIterations
[Optional] Specify the number of local iterations to be used for Local SGD algorithm. A value of 0 implies Local SGD is disabled. A value higher than 0 enables Local SGD and multiple, equal to the value supplied by the user. With Local SGD algorithm, the recommended values for arguments are:
  • LocalSGDIterations: 10
  • MaxIterNum: 100
  • BatchSize: 50
  • IterNumNoChange: 5
The value is a positive integer.
Default: 0