the efficiency of the model vs the robustness of the model

Both phrases are correct, but they convey different aspects of a model. 'The efficiency of the model' refers to how well the model performs in terms of speed and resource usage, while 'the robustness of the model' refers to how well the model performs under different conditions and with varying inputs.

Last updated: March 17, 2024 • 745 views

the efficiency of the model

This phrase is correct and commonly used in English to refer to how well a model performs in terms of speed and resource usage.

This phrase is used to describe how effectively a model operates in terms of speed, resource consumption, or performance.

Examples:

We need to improve the efficiency of the model to reduce processing time.
The efficiency of the model can be enhanced by optimizing the algorithm.
Measuring the efficiency of the model is crucial for performance evaluation.
The efficiency of the model directly impacts the overall system performance.
Efficiency is a key factor to consider when selecting a model for deployment.

Show examples from the web [+]

The second expresses the efficiency of the model under the assumption that it is to be used with an updating procedure to provide a forecast of discharge over a ...
Q is the mean of the observed discharge over the pre-selected period. Nash &. Sutcliffe (1970) then define the efficiency of the model E as the proportion of the.
Increasing the efficiency of the model change process, and reducing the risk of incorrect model changes, or changes that have unintended consequences.
the efficiency of the model stroke versus wrist-cock angle for a club-head mass of 200 g and a shaft length of 1.0 m. The plot clearly shows the considerable ...

Alternatives:

the performance of the model
the effectiveness of the model
the speed of the model
the resource utilization of the model
the processing efficiency of the model

the robustness of the model

This phrase is correct and commonly used in English to refer to how well a model performs under different conditions and with varying inputs.

This phrase is used to describe the ability of a model to maintain performance and accuracy across diverse datasets or scenarios.

Examples:

The robustness of the model was tested with various types of data.
A robust model should perform well even with noisy input.
Ensuring the robustness of the model is essential for real-world applications.
Robustness is a critical factor in evaluating the reliability of a model.
The robustness of the model can be improved through data augmentation techniques.

Show examples from the web [+]

we investigate the robustness of the model proposed by. Laub and Loomis for the molecular network underlying adenosine 3',5'-cycle monophosphate (cAMP) ...
Hence, the robustness of the model is demonstrated. The fact that the sizes of most factor loadings and error variances differed between both samples is most ...
... the scores of these features against each other, and generally investigate the robustness of the model webs to variation of the underlying definition of species, ...
Jun 12, 2015 ... The robustness of the model, its extrapolation potential, is then judged from the difference between the calibration and validation performance ...

Alternatives:

the resilience of the model
the stability of the model
the adaptability of the model
the versatility of the model
the consistency of the model

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the efficiency of the model vs the robustness of the model