### Related Doc: package optimization

:: DeveloperApi :: Compute gradient and loss for a Least-squared loss function, as used in linear regression. This is correct for the averaged least squares loss function (mean squared error) L = 1/2n ||A weights-y||^2 See also the documentation for the precise formulation.

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6. #### def compute(data: Vector, label: Double, weights: Vector, cumGradient: Vector): Double

Compute the gradient and loss given the features of a single data point, add the gradient to a provided vector to avoid creating new objects, and return loss.

Compute the gradient and loss given the features of a single data point, add the gradient to a provided vector to avoid creating new objects, and return loss.

data

features for one data point

label

label for this data point

weights

weights/coefficients corresponding to features

the computed gradient will be added to this vector

returns

loss

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7. #### def compute(data: Vector, label: Double, weights: Vector): (Vector, Double)

Compute the gradient and loss given the features of a single data point.

Compute the gradient and loss given the features of a single data point.

data

features for one data point

label

label for this data point

weights

weights/coefficients corresponding to features

returns

(gradient: Vector, loss: Double)

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