Bayesian Linear Regression Quiz

In Ridge regression, the loss function minimized is:Which term defines uncertainty in predictions in Bayesian Linear Regression?

• Bias

• Posterior variance

• Loss

• Covariance matrix of X

In Ridge regression, when λ = 0, the solution is equivalent to:

• Bayesian regression

• Logistic regression

• Least squares regression

• Lasso regression

Which component is updated using Bayes' Rule in Bayesian Linear Regression?

• Regularization term

• Prior

• Posterior

• Cost function

Bayesian Linear Regression helps prevent overfitting by:

• Removing features

• Shrinking weights

• Averaging over posterior distribution

• Maximizing likelihood only

Which matrix operation is involved in both Ridge and Bayesian Regression solutions?

• Inversion of the design matrix

• QR decomposition

• Determinant of covariance

• SVD of the prior

In Bayesian Linear Regression, what does the prior represent?

• Prediction distribution

• Belief before seeing the data

• Output space

• Loss function

Which parameter in Bayesian Linear Regression defines confidence in the prior?

• Noise variance

• λ

• Prior covariance

• Output mean

In Bayesian Linear Regression, the predictive distribution is obtained by:

• Point estimation

• Marginalizing over parameter distribution

• Gradient descent

• Sampling from the training data