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.
Selection of columns used as features. Columns with nominal and numerical data are allowed.
Selection of column(s) used as target(s). Only columns with numerical data are allowed.
Define whether missing values in the input data should be skipped or whether the node execution should fail on missing values.
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.
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