Cloudera
DS-200 · Question #55
Under what two conditions does stochastic gradient descent outperform 2nd-order optimization techniques such as iteratively reweighted least squares?
The correct answer is A. When the volume of input data is so large and diverse that a 2nd-order optimization technique B. When the model's estimates must be updated in real-time in order to account for. See the full explanation below for the reasoning.
Question
Under what two conditions does stochastic gradient descent outperform 2nd-order optimization techniques such as iteratively reweighted least squares?
Options
- AWhen the volume of input data is so large and diverse that a 2nd-order optimization technique
- BWhen the model's estimates must be updated in real-time in order to account for
- CWhen the input data can easily fit into memory on a single machine, but we want to calculate
- DWhen we are required to find the parameters that return the optimal value of the objective
How the community answered
(34 responses)- A74% (25)
- C9% (3)
- D18% (6)
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