Topic 1 Question 362
A data scientist uses Amazon SageMaker to perform hyperparameter tuning for a prototype machine leaming (ML) model. The data scientist's domain knowledge suggests that the hyperparameter is highly sensitive to changes.
The optimal value, x, is in the 0.5 < x < 1.0 range. The data scientist's domain knowledge suggests that the optimal value is close to 1.0.
The data scientist needs to find the optimal hyperparameter value with a minimum number of runs and with a high degree of consistent tuning conditions.
Which hyperparameter scaling type should the data scientist use to meet these requirements?
Auto scaling
Linear scaling
Logarithmic scaling
Reverse logarithmic scaling
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- 正解だと思う選択肢: D
This approach allocates more search effort near the higher end of the range, ensuring that values closer to 1.0 are explored more thoroughly, thus meeting the need for a minimum number of runs while maintaining consistent tuning conditions
👍 2MultiCloudIronMan2024/10/30 - 正解だと思う選択肢: D
A: No existe un escalado "auto" para este caso. B: El escalado lineal repartiría uniformemente el espacio y no se enfocaría en valores cercanos a 1.0. C: El escalado logarítmico no es ideal en este rango tan cercano a 1.0. D: El escalado logarítmico inverso concentra la búsqueda en valores cercanos a 1.0, aprovechando el conocimiento de dominio.
👍 1italiancloud20252025/02/18
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