model output

USAGE SUMMARY

The phrase "model output" is correct and usable in written English.
It can be used in contexts related to data analysis, machine learning, or any situation where the results produced by a model are being discussed. Example: "The model output indicates a significant improvement in accuracy compared to previous iterations."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

59 human-written examples

Yield is calculated from the model output.

International Journal of Geo-Engineering

i: model output value in the test i.

International Journal of Industrial Chemistry

The model output includes the following maps: 1.

Applied Water Science

The model output is depicted in Table 6.

Pastoralism

Corresponding model output is shown by solid and dotted lines.

Earth, Planets and Space

They are given based on the model output data.

Energy, Sustainability and Society

The calculated error was taken as model output.

Journal of Petroleum Exploration and Production Technology

Model output is showing increasing rainfall in the future.

Applied Water Science

AT and KM2 produced the same model output.

EJNMMI Research

In continuous time, the model output equations, respectively, are (3).

EURASIP Journal on Advances in Signal Processing

Model output data analysis supported the ground based station data.

Environmental Systems Research

Expert writing Tips

Best practice

Clearly define what constitutes the "model output" to avoid ambiguity, especially in complex models with multiple outputs.

Common error

Avoid using "model output" when you actually mean model input. The input is what you feed into the model, while the "model output" is the result or prediction generated by the model.

Linguistic Context

The phrase "model output" functions primarily as a noun phrase, referring to the data, results, or predictions generated by a model. Ludwig AI confirms that "model output" is correct and usable in written English, especially in data analysis and machine learning.

Expression frequency: Very common

Frequent in

Science

96%

Wiki

2%

News & Media

1%

Less common in

Formal & Business

1%

Encyclopedias

0%

Social Media

0%

Ludwig's WRAP-UP

In summary, "model output" is a noun phrase that denotes the results generated by a model, commonly used in scientific and technical fields. Ludwig AI confirms its correctness and widespread usage. The phrase serves to describe or refer to these results, facilitating analysis, validation, and interpretation. While it's most prevalent in formal and scientific contexts, it's essential to differentiate "model output" from "model input" to ensure clarity. Related phrases include "model results" and "simulation results", providing alternative ways to discuss the outcomes of a model.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

model results

Focuses on the numerical or qualitative findings derived from the model.

simulation results

Highlights the outcomes of a simulation-based model.

model predictions

Emphasizes the predictive capability of the model and its outcomes.

model outcomes

Broader term encompassing all possible results generated by the model.

model generated data

Highlights the data aspect of the output.

model's findings

Focuses on the insights and conclusions obtained from the model.

computational results

Emphasizes the computational nature of the results.

predicted values

Specifically refers to numerical outputs.

estimated parameters

Refers to estimated values of the model.

analysis results

Highlights the results from the analysis of the model's behaviour.

FAQs

How can I describe the results of a simulation instead of saying "model output"?

You can use alternatives like "simulation results", "predicted values", or "estimated parameters", depending on the specific nature of the results.

In what contexts is it most appropriate to use the term "model output"?

The term "model output" is particularly appropriate in technical, scientific, and academic contexts when discussing the results produced by a computational or mathematical model. It is also commonly used in data analysis and machine learning.

What's the difference between "model output" and "model performance"?

"Model output" refers to the actual results or predictions generated by a model. "Model performance", on the other hand, refers to how well the model performs against a set of metrics or benchmarks. You assess model performance based on the "model output".

How do I validate the "model output"?

Validating the "model output" typically involves comparing it against empirical data, experimental results, or real-world observations. Statistical methods can be used to quantify the agreement between the model's predictions and the observed data.