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 ascoeffNames
-
stdErr
: Vector of the standard error of the coefficients, with the same order ascoeffNames
-
zValue
(for logistic regression): Vector of z-values of the coefficients, in the same order ascoeffNames
-
tValue
(for linear regression): Vector of t-values of the coefficients, in the same order ascoeffNames
-
pValue
: Vector of p-values of the coefficients, in the same order ascoeffNames
-
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.