Home › Claude › aifoundations › linear_regression_and_supervised_learning › The design matrix X for n training examples with…
The design matrix X for n training examples with d features (plus intercept) has shape
An × (d + 1)
Bn × n
Cd × d
Dd × n
Answer & Solution
Correct answer: A. n × (d + 1)
X stacks training examples as ROWS; (d+1) columns include the d features plus the x₀ = 1 intercept term.
Related questions
The resulting per-example log-likelihood is, up to constantsWhy is STOCHASTIC gradient descent often preferred over BATCH gradient descent on very larClosed-form normal equations require XᵀX to beLogistic regression uses which hypothesis function?The PROBABILISTIC justification for the least-squares cost assumes the noise ε⁽ⁱ⁾ in y⁽ⁱ⁾ Why is the least-squares optimisation for LINEAR regression guaranteed to converge to the The closed-form 'normal equations' solution for least-squares linear regression isThe LMS (Widrow–Hoff) update rule on a single training example is