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It can reduce multicollinearity and overfitting (overfitting is a common problem in ML which means that the model can fit the training data well but cannot generalize to data outside the training set, such as the data in the testing set or data from new cases) problems through shrinkage strategy 7 , making it more resistant to multicollinearity than other ML methods like neural network 8

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Pisithkul, T., Patel, N

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