Sklearn Lasso Regression Learner (Labs)

Learns Lasso Regression implemented by scikit-learn library. It is a linear regression model trained with L1 prior as regularizer.

The model is trained with the selected numerical target column, and feature columns (can be numerical or nominal) from the input table. By default, the rightmost numerical column is selected as the target column and all the remaining numerical columns are selected as features.

Options

Input

Feature columns

Selection of columns used as feature columns. Columns with nominal and numerical data are allowed.

Target column

Selection of column used as the target column. Only columns with numerical data are allowed.

If there are missing values in the features

Define whether missing values in the input data should be skipped or whether the node execution should fail on missing values.

Available options:

  • Skip rows with missing values.: Rows with missing values will not be used for the training.
  • Fail on observing missing values.: Learner node will fail during the execution.

Algorithm Settings

Alpha

Constant that multiplies the L1 term, controlling regularization strength. alpha must be a non-negative float i.e. in [0, inf)

Calculate the intercept

Whether to calculate the intercept for the model. Specifies if a constant (a.k.a. bias or intercept) should be added to the decision function. If not selected, no intercept will be used in calculations meaning that the data is expected to be centered.

Maximum number of iterations

The maximum number of iterations run by the coordinate descent solver to converge (the algorithm used to fit the model is coordinate descent).

Input Ports

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Numerical and nominal columns can be selected as feature columns from this table, and the target column must be numerical.

Output Ports

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Trained Lasso regression model.

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