ability of the model

USAGE SUMMARY

The phrase "ability of the model" is correct and usable in written English.
It can be used when discussing the performance or capabilities of a particular model, often in contexts like machine learning or statistical analysis. Example: "The ability of the model to predict outcomes accurately is crucial for its effectiveness in real-world applications."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

60 human-written examples

This method provided the predictive ability of the model.

Clinical and Translational Medicine

Generalization ability of the model will also be investigated to overcome the problem of overfitting.

Chinese Journal of Mechanical Engineering

The comprehensive analytical ability of the model is commonly explained by the coefficient of determination (R2).

International Journal of Coal Science & Technology

We propose that this locality has some positive impact on the generalization ability of the model.

Journal of Cheminformatics

That increases the ability of the model to match the observed variability on empirical waiting distributions.

Health Economics Review

The ability of the model to describe experimental data is tested.

Polymer

The ability of the model to describe equilibrium swelling diagrams is confirmed by numerical simulation.

Polymer

Subsequently, the predicting ability of the model was assessed using the datasets for 14 unforeseen subjects.

Measurement

The extrapolation ability of the model is demonstrated by simulations of Fischer Tropsch waxes hydrocracking.

Advances in Chemical Engineering

The predictive ability of the model was evaluated using validation data set.

Journal of Molecular Graphics and Modelling

The ability of the model to simulate the partitioning behavior of tracers is also evaluated.

Developments in Water Science

Expert writing Tips

Best practice

When describing the "ability of the model", be specific about what aspect of the model's performance you are referring to. For instance, clarify whether you are discussing its predictive, descriptive, or generalization ability.

Common error

Avoid exaggerating the "ability of the model" without sufficient evidence. Ensure that claims about its capabilities are supported by empirical data and rigorous testing.

Linguistic Context

The phrase "ability of the model" functions as a noun phrase, typically acting as the subject or object of a sentence. As evidenced by Ludwig, it describes the capacity or capability of a model to perform a specific task or function.

Expression frequency: Very common

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Encyclopedias

0%

Ludwig's WRAP-UP

In summary, the phrase "ability of the model" is a grammatically correct and commonly used noun phrase that describes the capacity or performance of a model, particularly in scientific and technical contexts. Ludwig AI confirms its validity and frequent usage in describing a model's predictive, descriptive, or generalization skills. To enhance clarity in writing, it's advisable to specify which aspect of the model's performance is being discussed. Common alternatives include "model's capability" and "model's performance". When using this phrase, ensure claims are supported by empirical evidence to avoid overstatement. The phrase's function is primarily evaluative, with a formal register often seen in academic and scientific publications. Therefore, understanding and correctly using "ability of the model" is crucial for technical communication.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

model's capability

Replaces "ability" with "capability", focusing on the inherent potential of the model.

model's performance

Shifts the focus to the actual performance rather than the potential.

capacity of the model

Emphasizes the model's maximum potential or output.

effectiveness of the model

Highlights how well the model achieves its intended purpose.

model's predictive power

Specifically refers to the model's ability to make accurate predictions.

model's aptitude

Suggests a natural talent or skill possessed by the model.

potential of the model

Focuses on the undeveloped capabilities that the model could exhibit.

model's competence

Indicates that the model is adequately qualified or capable.

strength of the model

Highlights the robustness and reliability of the model.

model's skill

Draws attention to the refined expertise demonstrated by the model.

FAQs

How can I assess the "ability of the model"?

The "ability of the model" can be assessed using various metrics depending on the model's purpose. For predictive models, metrics like accuracy, precision, and recall are useful. For models focused on explaining variance, R-squared or adjusted R-squared can be used. Additionally, techniques like cross-validation and the "Hosmer-Lemeshow goodness-of-fit test" can evaluate overall model fit.

What does it mean to improve the "ability of the model"?

Improving the "ability of the model" typically means enhancing its performance in its intended task. This could involve increasing its accuracy, reducing errors, or expanding its range of applicability. Techniques to improve the model include feature engineering, hyperparameter tuning, or using a different modeling algorithm.

What are some alternatives to "ability of the model"?

Some alternatives to "ability of the model" include "model's capability", "model's performance", "effectiveness of the model", or "predictive power of model". The choice depends on the specific context and the aspect of the model you wish to emphasize.

How does the "ability of the model" relate to its complexity?

The "ability of the model" is often related to its complexity, but not always in a straightforward manner. More complex models can capture intricate patterns in data, potentially increasing predictive ability. However, overly complex models risk overfitting, reducing their ability to generalize to new data. Therefore, finding the right balance between complexity and generalization is crucial.