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
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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