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)
Parameters:
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.
Example:
Python3
import numpy as npimport pandas as pdfrom sklearn import datasetsimport matplotlib.pyplot as pltfrom sklearn.cross_decomposition import PLSRegressionfrom sklearn.model_selection import train_test_split# load boston data using sklearn datasetsboston = datasets.load_boston()# separate data and target valuesx = boston.datay = boston.target# tabular data structure with labeled axes # (rows and columns) using DataFramedf_x = pd.DataFrame(x, columns=boston.feature_names)df_y = pd.DataFrame(y)# create PLSRegression modelpls2 = PLSRegression(n_components=2)# split datax_train, x_test, y_train, y_test = train_test_split( df_x, df_y, test_size=0.30, random_state=1)# fit the modelpls2.fit(x_train, y_train)# predict the valuesY_pred = pls2.predict(x_test)# plot the predicted Valuesplt.plot(Y_pred)plt.xticks(rotation=90)plt.show()# print the predicted valueprint(Y_pred) |
Output:
Plot the Predicted value using PLSRegression
Print the predicted value using trained model
