poor generalization

USAGE SUMMARY

The phrase "poor generalization" is correct and usable in written English.
It can be used to describe a conclusion or statement that is overly broad or not adequately supported by evidence. Example: "The study's findings were criticized for making a poor generalization about the entire population based on a small sample size."

✓ Grammatically correct

Science

Academia

Human-verified examples from authoritative sources

Exact Expressions

53 human-written examples

The simulation reported by TKS showed relatively poor generalization to novel items.

Stanford University

Classification approaches usually present the poor generalization performance with an apparent class imbalance problem.

Expert Systems with Applications

Despite the surge of proposals, research has shown existing methods possess a very shallow understanding of text, which is reflected in their poor generalization capabilities.

Stanford University

However, the datasets obtained from real experiments are likely to contain outliers or noises, which can lead to poor generalization ability and classification accuracy.

Computers in Biology and Medicine

Overfitting indicates that precise predication can be drawn from the training set but the performance is low when using testing dataset (i.e., the trained model has poor generalization ability to predict new cases).

Nature

The designed early warning method overcomes the difficulties of lack of large-scale and high quality training samples,the lack causing the traditional RBFNN's poor generalization ability.

Procedia Engineering

Human-verified similar examples from authoritative sources

Similar Expressions

7 human-written examples

However, empirical studies include different descriptors measured at different spatial scales, producing poor generalizations.

Agriculture, Ecosystems & Environment

We do not expect the authors to be at fault for nonsensical arguments based on poor generalizations.

BMC Medical Genomics

This paper addressed one of the most important shortcomings of hard decision tree-based context-dependent F0 modeling, namely, poor context generalization.

EURASIP Journal on Advances in Signal Processing

This leads to overfitting characterized by a model performing excellently well in training but poor in generalization on new cases (Hastie et al. 2009).

International Journal of Concrete Structures and Materials

Current vapor pressure models, which utilize traditional physical properties as inputs, are limited by their range of applicability and/or by poor suitability for generalization.

Fluid Phase Equilibria

Expert writing Tips

Best practice

When using "poor generalization", ensure you provide specific reasons why the generalization is considered poor, such as insufficient data, biased samples, or flawed logic.

Common error

Avoid exaggerating the consequences of "poor generalization". Instead of claiming it invalidates an entire study, focus on its limited applicability or potential for misleading conclusions.

Linguistic Context

The phrase "poor generalization" typically functions as a noun phrase, where "poor" modifies the noun "generalization". It describes a deficiency or inadequacy in the process of forming broad conclusions or inferences. Ludwig shows that it is a common term in academic and scientific contexts.

Expression frequency: Very common

Frequent in

Science

76%

Academia

20%

News & Media

2%

Less common in

Formal & Business

1%

Encyclopedias

0%

Wiki

0%

Ludwig's WRAP-UP

The phrase "poor generalization" is a common and grammatically correct term used primarily in scientific and academic writing to describe the limitations of findings or models in their applicability to broader contexts. According to Ludwig, this issue often arises due to factors like small sample sizes, biased data, or overfitting. To mitigate "poor generalization", strategies include increasing data diversity, employing regularization techniques, and conducting cross-validation. Understanding the causes and consequences of "poor generalization" is crucial for ensuring the reliability and validity of research and modeling efforts.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

weak generalization

Emphasizes the feebleness or lack of strength in the generalization.

flawed generalization

Highlights the presence of errors or defects in the generalization process.

inadequate generalization

Suggests that the generalization is insufficient or not good enough.

limited generalization

Indicates that the generalization only applies to a restricted set of cases.

unsound generalization

Implies that the generalization lacks a solid foundation or basis.

deficient generalization

Focuses on the lack or absence of necessary qualities in the generalization.

faulty generalization

Similar to flawed, but emphasizes the mistake or error in the generalizing process.

imperfect generalization

Highlights that the generalization isn't complete or ideal.

tenuous generalization

Suggests that the generalization is weak and easily challenged.

dubious generalization

Indicates that the generalization is questionable or uncertain.

FAQs

What does "poor generalization" mean in the context of machine learning?

In machine learning, "poor generalization" refers to a model's inability to accurately predict outcomes on new, unseen data after being trained on a specific dataset. This is often due to overfitting the training data.

How can I improve generalization in a model that exhibits "poor generalization"?

Techniques such as increasing the size and diversity of the training dataset, using regularization methods, employing cross-validation, or simplifying the model can help improve "generalization performance".

What are some common causes of "poor generalization" in research studies?

Common causes include small sample sizes, biased sampling methods, failure to account for confounding variables, and over-reliance on statistical significance without considering practical significance.

Is "poor generalization" the same as overfitting?

Overfitting is a primary cause of "poor generalization". When a model is overfit, it performs well on the training data but fails to generalize to new data. However, "poor generalization" can also arise from other issues, such as "biased data" or "flawed methodology".