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.
In this article, we will know how to solve the image classification problem using a convolutional neural network aka cnn in python.
Moreover, ridge regression and lasso regression are ways of applying regularization. These two regression techniques can be differentiated on the basis of the techniques being employed by them to add a penalty.
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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.
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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