Configuring Advanced Options

  1. Leave Ignore constant fields checked to skip fields that have the same value for each record.
  2. Check Compute p values to calculate p values for the parameter estimates.
  3. Check Remove collinear column to automatically remove collinear columns during model building. This will result in a 0 coefficient in the returned model.
    This option must be checked if Compute p values is also checked.
  4. Leave Include constant term (Intercept) checked to include a constant term (intercept) in the model.
    This field must be checked if Remove collinear column is also checked.
  5. Select a Solver from the drop-down list. Note that CoordinateDescent and CoordinateDescentNaive are currently experimental.
    Auto
    Solver will be determined based on input data and parameters.
    CoordinateDescent
    IRLSM with the covariance updates version of cyclical coordinate descent in the innermost loop.
    CoordinateDescentNaive
    IRLSM with the naive updates version of cyclical coordinate descent in the innermost loop.
    IRLSM
    Ideal for problems with a small number of predictors or for Lambda searches with L1 penalty.
    LBFGS
    Ideal for datasets with many columns.
  6. Leave Seed for N fold checked and enter a seed number to ensure that when the data is split into test and train data it will occur the same way each time you run the dataflow. Uncheck in this field to get a random split each time you run the flow.
  7. Check N fold and enter the number of folds if you are performing cross-validation.
  8. Click Fold assignment and select from the drop-down list if you are performing cross-validation. This field is applicable only if you entered a value in N fold and Fold field is not specified.
    Auto

    Allows the algorithm to automatically choose an option; currently it uses Random.

    Modulo

    Evenly splits the dataset into the folds and does not depend on the seed.

    Random

    Randomly splits the data into nfolds pieces; best for large datasets.

  9. If you are performing cross-validation, check Fold field and select the field that contains the cross-validation fold index assignment from the drop-down list.
    This field is applicable only if you did not enter a value in N fold and Fold assignment.
  10. Check Maximum iterations and enter the number of training iterations that should take place.
  11. Check Objective epsilon and enter the threshold for convergence; this must be a value between 0 and 1. If the objective value is less than this threshold, the model will be converged.
  12. Check Beta epsilon and enter the threshold for convergence; this must be a value between 0 and 1. If the objective value is less than this threshold, the model will be converged. If the L1 normalization of the current beta change is below this threshold, consider using convergence.
  13. Click OK to save the model and configuration or continue to the next tab.