capability of generalization

USAGE SUMMARY

The phrase "capability of generalization" is a correct and usable phrase in written English.
You can use this phrase when discussing a person or entity's ability to identify patterns or understand concepts that extend beyond just a single isolated example. For example, "The data scientist demonstrated an impressive capability of generalization, noting the correlation between the two seemingly disparate datasets."

✓ Grammatically correct

Science

Human-verified examples from authoritative sources

Exact Expressions

6 human-written examples

The resultant regression has the capability of generalization in the studied field.

Journal of Petroleum Science and Engineering

The experimental results indicate that the best predictive accuracy and capability of generalization was achieved for SMA containing Mixed RCA RMSEE = 25.20119) while the worst predictive accuracy and capability of generalization was achieved for HMA containing Coarse RCA RMSEE = 35.56637).

Construction and Building Materials

The experimental outcomes suggested that ANFIS approach can be used to improve predictive accuracy and capability of generalization.

Construction and Building Materials

In this way, the capability of generalization of the MLP to work with data sets never processed during the training stage is maintained.

EURASIP Journal on Advances in Signal Processing

The first one aims to directly classify the high-dimensional data while keeping the good capability of generalization and efficiency in optimization.

BMC Systems Biology

First of all, we have to face to the cures of high dimensionality which means that many machine learning models can be overfitted and therefore have poor capability of generalization.

BMC Systems Biology

Human-verified similar examples from authoritative sources

Similar Expressions

54 human-written examples

In this way, the data from some weather stations in the Basque Country and Valencia region (Spain) were used for training the neuro-fuzzy models [in humid and non-humid regions, respectively] and subsequently, the data from these regions were pooled to evaluate the generalization capability of a general neuro-fuzzy model in humid and non-humid regions.

Journal of Hydrology

It is well known that no algorithm can hold a general advantage in terms of generalization capability over another one across all possible classification tasks.

BioMed Research International

Moreover, sensitivity analysis of both models also proves efficiency of the prediction capability and generalization of the data.

Chemometrics and Intelligent Laboratory Systems

So for the sake of generalization capability of our model, it is much practical to use only gene expression to construct prediction model rather than all genomic features.

BMC Cancer

These pattern recognition techniques have shown excellent performance in numerous practical applications, especially in terms of generalization capabilities, such as handwritten character recognition, three-dimensional object recognition, or remote sensing [27].

EURASIP Journal on Advances in Signal Processing

Expert writing Tips

Best practice

When discussing the "capability of generalization" in machine learning models, explicitly define the scope or domain to which the generalization applies. This adds clarity and avoids overstating the model's applicability.

Common error

Avoid assuming that a model with high "capability of generalization" in one context will automatically perform well in unrelated domains. Always validate the model's performance in the target environment.

Linguistic Context

The phrase "capability of generalization" functions as a noun phrase that describes the ability or capacity to extend knowledge or models learned from specific instances to a broader range of situations or data. As Ludwig AI confirms, this phrase is correct. The provided examples in Ludwig show the phrase used to describe models, algorithms, and approaches.

Expression frequency: Uncommon

Frequent in

Science

100%

Less common in

News & Media

0%

Formal & Business

0%

Academia

0%

Ludwig's WRAP-UP

The phrase "capability of generalization" describes the ability of a model or system to apply learned knowledge to new, unseen data. As confirmed by Ludwig AI, the phrase is grammatically correct and primarily used in scientific contexts. While not extremely common, it effectively conveys the robustness of a model in handling different scenarios. Related phrases like "ability to generalize" or "potential for generalization" offer similar meanings with slight variations. When using the phrase, it's crucial to define the scope of generalization and avoid assuming its applicability across unrelated domains.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

ability to generalize

Replaces "capability" with "ability" for a more concise expression, retaining the core meaning.

capacity for generalization

Substitutes "capability" with "capacity", emphasizing the potential for generalization.

potential for generalization

Focuses on the inherent possibility of generalizing from specific instances.

power of generalization

Emphasizes the strength and effectiveness of the ability to generalize.

aptitude for generalization

Highlights a natural inclination or talent for generalizing.

skill in generalization

Replaces capability with skill to emphasize expertise.

facility for generalization

Suggests an ease or smoothness in the process of generalizing.

effectiveness of generalization

Shifts the focus to how well the generalization performs in new situations.

generalizability

Uses a single word to convey the extent to which something can be generalized.

transfer learning ability

Emphasizes the machine learning context of applying knowledge from one task to another, closely related task.

FAQs

How can I improve the "capability of generalization" in a machine learning model?

Techniques such as regularization, data augmentation, and cross-validation can help to improve the "generalization ability" of machine learning models.

What's the difference between "capability of generalization" and overfitting?

"Capability of generalization" refers to a model's ability to perform well on unseen data, while overfitting occurs when a model learns the training data too well, resulting in poor performance on new data. Aiming to have a good "ability to generalize" means avoiding overfitting.

In what contexts is the "capability of generalization" particularly important?

The "capability of generalization" is crucial in scenarios where models need to be deployed in environments different from those they were trained on, such as in medical diagnosis or financial forecasting.

What are some methods to evaluate the "capability of generalization"?

Common methods include using held-out test sets, cross-validation, and evaluating performance on benchmark datasets that represent real-world scenarios.