After applying SVD() in Surprise ,internally 3-matrices should be created. how to view them individually?? the code is listed below

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#importing packages
from surprise import SVD
from surprise import Dataset
from surprise import accuracy
import numpy as np
import pandas as pd
from surprise.model_selection import train_test_split

#Load movielens-100k dataset
required_data = Dataset.load_builtin('ml-100k')

#Dividing the data in test and train
trainset, testingset = train_test_split(required_data,test_size=.25)
#initializing the SVD Algorithm
model_algo = SVD(n_factors=19)
#Fitting the model
model_algo.fit(trainset)
model_algo.qi.shape
model_algo.qi

what should i write further to view that [user x factors] and other matrices?

svd python
1 Answers

[user x latentfactors] matrix can be viewed using model_algo.pu

[latentfactors x Items] matrix can be viewed using model_algo.qi

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