Need for regularization
Machine learning models tend to overfit, making accurate predictions on trained data but inaccurate ones on new examples. Regularization helps prevent overfitting by shrinking the parameters through a regularized term added to the cost function.
Check out my comic that explains regularization in a fun and simple way!
Regularization calculation
Let's take an example of a linear regression model.
From my cost function blog, we can define the minimization of the linear regression cost function as:
Where,
J(θ) - cost function of linear regression
M - number of training examples
hθ(xi) - predicted output for the i-th input x, given the parameters θ
θ - parameters like model weights
yi - actual output for the input xi
As we can see, in the overfitting graph there are too many parameters.
We need to multiply the θ3 and θ4 values with a number to make it really small.
Therefore, θ3 and θ4 become negligible as the min function is present.
Now, the generalized equation for linear regression regularization can be written as:
Where,
λ - regularization parameter (penalizing value)
n - number of parameters
Similar to the linear regression example we've taken, any function can be regularized by adding a shrinking term.
Hope you've understood the concept of regularization! Let me know what you think :)
Awesome!