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sklearn.cross_decomposition.PLSRegression() function in Python
  • Last Updated : 25 Oct, 2020

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

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import numpy as np
import pandas as pd
from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn.cross_decomposition import PLSRegression
from sklearn.model_selection import train_test_split
  
  
# load boston data using sklearn datasets
boston = datasets.load_boston()
  
# seprate data and target values
x = boston.data
y = boston.target
  
# tabular data structure with labeled axes 
# (rows and columns) using DataFrame
df_x = pd.DataFrame(x, columns=boston.feature_names)
df_y = pd.DataFrame(y)
  
# create PLSRegression model
pls2 = PLSRegression(n_components=2)
  
# split data
x_train, x_test, y_train, y_test = train_test_split(
    df_x, df_y, test_size=0.30, random_state=1)
  
# fit the model
pls2.fit(x_train, y_train)
  
# predict the values
Y_pred = pls2.predict(x_test)
  
# plot the predicted Values
plt.plot(Y_pred)
plt.xticks(rotation=90)
plt.show()
  
# print the predicted value
print(Y_pred)

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Output:

Plot the Predicted value using PLSRegression

Print the predicted value using trained model


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