regression-model-attributes.md

data
The data for the function, including:
  • coeffNames: Name of the coefficients. This starts with intercept and then follows with the names of the predictors in the same order specified in the call.

  • coeff: Vector of estimated coefficients, with the same order as coeffNames

  • stdErr: Vector of the standard error of the coefficients, with the same order as coeffNames

  • zValue (for logistic regression): Vector of z-values of the coefficients, in the same order as coeffNames

  • tValue (for linear regression): Vector of t-values of the coefficients, in the same order as coeffNames

  • pValue: Vector of p-values of the coefficients, in the same order as coeffNames

regularization
Type of regularization to use when training the model.
lambda
Regularization parameter. Higher values enforce stronger regularization. This value must be nonnegative.
alpha
Elastic net mixture parameter.
iterations
Number of iterations that actually occur for the convergence before exceeding max_iterations.
skippedRows
Number of rows of the input relation that were skipped because they contained an invalid value.
processedRows
Total number of input relation rows minus skippedRows.
callStr
Value of all input arguments specified when the function was called.