- OUT clause
- Accepts the MetaInformationTable and RBFFitTable clauses.
- ModelType
- Specifies the type of the analysis. Acceptable values are Regression, Classification.
Default: Classification
- Kernel
- Specifies the type of kernel used to create the model. Acceptable values are Linear and RBF.
Default: Linear
- NComponents
- This hyperparameter is only used if the 'Kernel' is set to 'RBF' (non-linear kernel).
Specifies the number of Monte Carlo samples per original feature. Equals the dimensionality of the computed feature space.
Default: 100
- Gamma
- This hyperparameter is only used if the 'Kernel' is set to 'RBF' (non-linear kernel).
Gamma defines the influence radius of a training example. The value should normally be between 3/k and 6/k, where k is the number of input fields.
For example, if there are 12 input fields, values between 0.25 and 0.5 would be worth trying. Increasing the value improves the classification accuracy (or reduces the regression error) for the training data, but this can also lead to overfitting.
- MaxIterNum
- Specifies the maximum number of iterations (minibatches) over the training data batches. The value is a positive integer less than 10,000,000.
Default: 300
- Epsilon
- Specifies 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.
Epsilon is a numeric value that sets a threshold for the epsilon-insensitive loss, which means that any difference between the predicted value and the actual label within this threshold is ignored.
This means that the SVM algorithm allows for a margin of error, or "slack" when making predictions for regression problems. The value of epsilon determines the size of this margin of error and affects the tradeoff between the accuracy of the model and its ability to generalize to new data.
Default: 0.1
- BatchSize
- Specifies 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 positive integer.
Default: 10
- RegularizationLambda
- Specifies the amount of regularization to be added. The higher the value, stronger the regularization. It must be a positive float value. A value of 0 means no regularization.
RegularizationLambda is a positive float value that specifies the amount of regularization to be added to the model. The higher the value of RegularizationLambda, the stronger the regularization, which means that the algorithm places a greater emphasis on preventing overfitting to the training data.
In addition to its use in regularization, RegularizationLambda is also used to compute the learning rate when learning rate is set to optimal. Learning rate is a parameter that controls the step size at each iteration of the optimization algorithm and setting it to optimal means that it is automatically determined based on the value of RegularizationLambda.
Default: 0.02
- Alpha
- Specifies 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
- Specifies 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 positive integer.
Default: 50
- Tolerance
- Specifies 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
- Specifies whether intercept is to be estimated based on whether data is already centered.
Default: True
- ClassWeights
- Specifies 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 compared to class 1. If the weight of a class is omitted, it is assumed to be 1.0.
Default: 0:1.0, 1:1.0
- LearningRate
- Specifies the learning rate algorithm. Learning rates are:
- Constant
- InvTime
- Optimal
- Adaptive
Default: InvTime for Regression, Optimal for Classification
- InitialEta
- Specifies the initial value of eta for learning rate. For LearningRate set to constant, this value is the learning rate for all iterations.The learning rate controls how much the SVM algorithm adjusts the weights of the model during training. If the learning rate is too low, the model may converge slowly. If the learning rate is too high, the model may fail to converge at all.
The choice of learning rate can have a significant impact on the accuracy and speed of the SVM algorithm, so it is an important parameter to consider when training a model.
Default: 0.05
- DecayRate
- Specifies the decay rate for learning rate. Only applicable for learning rates invtime and adaptive.
Default: 0.25
- DecaySteps
- Specifies 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
- Specifies 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. A value between 0.6-0.95 is recommended. The value is a positive float between 0 and 1.
Default: 0
- Nesterov
- Specifies whether to apply the Nesterov optimization to Momentum optimizer or not. Only applicable when Momentum is greater than 0.
Default: False
- LocalSGDIterations
- Specifies the number of local iterations to be used for Local SGD algorithm. A value of 0 means 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