sklearn.cross_decomposition.PLSRegression() function in Python
PLS regression is a Regression method that takes into account the latent structure in both datasets. Partial least squares regression performed well in MRI-based assessments for both single-label and multi-label learning reasons. PLSRegression acquires from PLS with mode=”A” and deflation_mode=”regression”. Additionally, known PLS2 or PLS in the event of a one-dimensional response.
Syntax: class sklearn.cross_decomposition.PLSRegression(n_components=2, *, scale=True, max_iter=500, tol=1e-06, copy=True)
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This function accepts five parameters which are mentioned above and defined below:
- n_components:<int>: Its default value is 2, and it accepts the number of components that are needed to keep.
- scale:<bool>: Its default value is True, and it accepts whether to scale the data or not.
- max_iteran :<int>: Its default value is 500, and it accepts the maximum number of iteration of the NIPALS inner loop.
- tol: <non-negative real>: Its default value is 1e-06, and it accepts tolerance used in the iterative algorithm.
- copy:<bool>: Its default value is True, and it shows that deflection should be done on a copy. Don’t care about side effects when the default value is set True.
Return Value: PLSRegression is an approach for predicting response.
The below Example illustrates the use of the PLSRegression() Model.
Plot the Predicted value using PLSRegression
Print the predicted value using trained model