Why would you want to use lasso instead of ridge regression?

Lasso method overcomes the disadvantage of Ridge regression by not only punishing high values of the coefficients β but actually setting them to zero if they are not relevant. Therefore, you might end up with fewer features included in the model than you started with, which is a huge advantage.

What is the advantage of using lasso over ridge regression?

One obvious advantage of lasso regression over ridge regression, is that it produces simpler and more interpretable models that incorporate only a reduced set of the predictors. However, neither ridge regression nor the lasso will universally dominate the other.

What Gaussian prior?

In ridge regression, a gaussian prior on regression coefficients means that the coefficients are assumed to be distributed according to Gaussian/Normal distribution.

What is Laplace distribution used for?

The Laplace distribution is used for modeling in signal processing, various biological processes, finance, and economics. Examples of events that may be modeled by Laplace distribution include: Credit risk and exotic options in financial engineering.

What is the difference between Lasso and Ridge regression?

Lasso is a modification of linear regression, where the model is penalized for the sum of absolute values of the weights. Ridge takes a step further and penalizes the model for the sum of squared value of the weights.

What is one advantage of using lasso over ridge regression for a linear regression problem?

It all depends on the computing power and data available to perform these techniques on a statistical software. Ridge regression is faster compared to lasso but then again lasso has the advantage of completely reducing unnecessary parameters in the model.

Why use a Gaussian prior?

Why do we use Gaussian process as a model for the data? Realizations of Gaussian processes with a proper covariance function can provide nearly all functions we can encounter in “real life”. Also, they are convenient and provide exact inference and marginal distribution.

Why Laplace distribution is called double exponential distribution?

It is also sometimes called the double exponential distribution, because it can be thought of as two exponential distributions (with an additional location parameter) spliced together back-to-back, although the term is also sometimes used to refer to the Gumbel distribution. …