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.. _alg-enmapbox-fitplsregression:

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Fit PLSRegression
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Partial Least Squares `regression <https://enmap-box.readthedocs.io/en/latest/general/glossary.html#term-regression>`_.

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Partial Least Squares (PLS) regression is a statistical technique used for modeling the relationship between a set of independent variables (features or predictors) and a dependent variable (target) when there is multicollinearity and when there are more predictors than observations. PLS combines elements of principal component analysis and multiple linear regression to find a linear relationship between the variables while reducing the dimensionality of the predictor space.

Usage:

1. Start the algorithm from the Processing Toolbox panel.

2. Select a training dataset or create one by clicking the processing algorithm icon, then click :guilabel:`run`.

    .. figure:: ../../processing_algorithms/regression/img/pls.png
       :align: center

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**Parameters**

:guilabel:`Regressor` [string]
    Scikit-learn python code. See `PLSRegression <https://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html>`_ for information on different parameters.
    Default::

        from sklearn.cross_decomposition import PLSRegression

        regressor = PLSRegression\(n_components=2\)

:guilabel:`Training dataset` [file]
    `Training dataset <https://enmap-box.readthedocs.io/en/latest/general/glossary.html#term-training-dataset>`_ `pickle file <https://enmap-box.readthedocs.io/en/latest/general/glossary.html#term-pickle-file>`_ used for fitting the `classifier <https://enmap-box.readthedocs.io/en/latest/general/glossary.html#term-classifier>`_. If not specified, an unfitted classifier is created.

**Outputs**

:guilabel:`Output regressor` [fileDestination]
    `Pickle file <https://enmap-box.readthedocs.io/en/latest/general/glossary.html#term-pickle-file>`_ destination.

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**Command-line usage**

``>qgis_process help enmapbox:fitplsregression``::

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    Arguments
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    regressor: Regressor
        Default value:    from sklearn.cross_decomposition import PLSRegression

    regressor = PLSRegression(n_components=2)
        Argument type:    string
        Acceptable values:
            - String value
            - field:FIELD_NAME to use a data defined value taken from the FIELD_NAME field
            - expression:SOME EXPRESSION to use a data defined value calculated using a custom QGIS expression
    dataset: Training dataset (optional)
        Argument type:    file
        Acceptable values:
            - Path to a file
    outputRegressor: Output regressor
        Argument type:    fileDestination
        Acceptable values:
            - Path for new file

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    Outputs
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    outputRegressor: <outputFile>
        Output regressor

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