Partial Least Squares Regression Learner (sklearn)

Learns partial least squares regression implemented by scikit-learn library.

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

If there are at least two numerical columns and two other numerical or nominal columns available, rightmost two numerical columns are selected as targets and all the remaining columns are selected as features by default. If there are only two numerical (or one numerical and one nominal) columns are available, the rightmost column is selected as the target and the other column is selected as the feature by default.



Feature columns

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

Target columns

Selection of column(s) used as target(s). 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

Number of components to keep

The number of basic components to compute. Should be in [1, min(number_of_samples, number_of_features, number_of_targets)]. Number of samples is the number of rows in the input table.

Input Ports


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

Output Ports


Trained partial least squares regression model.

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