We will learn to develop a Bone Fracture Detection using Ridge Model. Read the requirements file for installation process
Ridge regression is a type of regularized linear regression method that adds a penalty term to the ordinary least squares objective function. This penalty term shrinks the coefficient estimates towards zero, which helps to reduce the variance in the estimates and prevent overfitting.
A ridge regression model uses L2 regularization, which adds a penalty term that is proportional to the square of the magnitude of the coefficients. The hyperparameter alpha controls the strength of the regularization, with higher values of alpha leading to stronger regularization and smaller coefficient estimates.
In practice, ridge regression is often used when there are many predictor variables and some of them are highly correlated.
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