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Regularization is a process in machine learning which is applied to control the overfitting of the data, and this overfitting occurs when the training dataset and the testing dataset vary too much. Therefore, regularization is implemented in algorithms such as linear regression to reduce overfitting.
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Decision trees are a non-parametric form of supervised machine learning algorithm used for both classification and regression. As the name suggests, decision trees work by asking a Boolean form of question and, based on the answer, make a decision that goes further in the form of a tree, thus the name decision tree.
In this article, we are going to look at how the polynomial regression is of assistance to us while we are working with the machine learning projects with the help of R. We have also studied how linear regression is useful to us while dealing with various machine learning problems
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Regularization is a process in machine learning which is applied to control the overfitting of the data, and this overfitting occurs when the training dataset and the testing dataset vary too much. Therefore, regularization is implemented in algorithms such as linear regression to reduce overfitting.
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