Linear Regression

Creates a linear regression model.

This operation is ported from Spark ML.

For a comprehensive introduction, see Spark documentation.

For scala docs details, see org.apache.spark.ml.regression.LinearRegression documentation.

Since: Seahorse 1.0.0

Input

This operation does not take any input.

Output

Port Type Qualifier Description
0EstimatorAn Estimator that can be used in a Fit operation.

Parameters

Name Type Description
elastic net param Numeric The ElasticNet mixing parameter. For alpha = 0, the penalty is an L2 penalty. For alpha = 1, it is an L1 penalty.
fit intercept Boolean Whether to fit an intercept term.
max iterations Numeric The maximum number of iterations.
regularization param Numeric The regularization parameter.
tolerance Numeric The convergence tolerance for iterative algorithms.
standardization Boolean Whether to standardize the training features before fitting the model.
use custom weights SingleChoice Whether to over-/under-sample training instances according to the given weights in the `weight column`. If the `weight column` is not specified, all instances are treated equally with a weight 1.0. Possible values: ["no", "yes"]
solver SingleChoice Sets the solver algorithm used for optimization. Can be set to "l-bfgs", "normal" or "auto". "l-bfgs" denotes Limited-memory BFGS which is a limited-memory quasi-Newton optimization method. "normal" denotes Normal Equation. It is an analytical solution to the linear regression problem. The default value is "auto" which means that the solver algorithm is selected automatically. Possible values: ["auto", "normal", "l-bfgs"]
label column SingleColumnSelector The label column for model fitting.
features column SingleColumnSelector The features column for model fitting.
prediction column String The prediction column created during model scoring.