alleryStart learning →The PROBABILISTIC justification for the least-squares cost assumes the noise ε⁽ⁱ⁾ in y⁽ⁱ⁾ = θᵀx⁽ⁱ⁾ + ε⁽ⁱ⁾ is
ABernoulli(p)
BUniform on [0, 1]
CIid Gaussian N(0, σ²)
DExponential with rate 1
Answer & Solution
Correct answer: C. Iid Gaussian N(0, σ²)
Under iid Gaussian noise N(0, σ²), maximising the likelihood is equivalent to minimising the sum of squared errors — that's why least-squares is 'the' regression loss.
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