Fit PLSRegression

Partial Least Squares regression.

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:

Start the algorithm from the Processing Toolbox panel.
Select a training dataset or create one by clicking the processing algorithm icon, then click run.

Parameters

Regressor [string]

Scikit-learn python code. See PLSRegression for information on different parameters.

Default:

from sklearn.cross_decomposition import PLSRegression

regressor = PLSRegression(n_components=2)

Training dataset [file]

Training dataset pickle file used for fitting the classifier. If not specified, an unfitted classifier is created.

Outputs

Output regressor [fileDestination]: Pickle file destination.

Command-line usage

>qgis_process help enmapbox:FitPlsregression:

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Arguments
----------------

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
----------------

outputRegressor: <outputFile>
    Output regressor