cross validate

USAGE SUMMARY

The phrase "cross validate" is correct and usable in written English.
It is typically used in the context of data analysis or machine learning to refer to the process of validating a model's performance by dividing the data into subsets. Example: "To ensure the accuracy of our predictive model, we need to cross validate it using different data sets."

✓ Grammatically correct

Science

Science & Research

Human-verified examples from authoritative sources

Exact Expressions

8 human-written examples

Therefore, NMR and PIA derived pore sizes can be used to cross validate each other.

Journal of Petroleum Science and Engineering

Experimental investigation is carried out to cross validate the characteristics of the developed flexure-based mechanism.

Sensors and Actuators A: Physical

The proposed algorithm and the algorithm in [10] can be used to cross validate each other.

EURASIP Journal on Image and Video Processing

The leave-one-out strategy is followed to cross validate the performance of the virtual sensing model.

Petroleum Science

The method of Cheadle et al [32] was used to cross validate the entire dataset between chip platforms.

Plosone

Sixth, we used bootstrap to cross validate our model.

BMC Musculoskeletal Disorders

Human-verified similar examples from authoritative sources

Similar Expressions

52 human-written examples

In order to cross-validate the models and minimize overfitting, a leave-one-out methodology was applied to each model.

Nature

Our aim was to cross-validate electrocardiographic (ECG) and scintigraphic imaging of acute myocardial ischemia.

Journal of Electrocardiology

A multi-group analysis with gender and age was performed to cross-validate it.

Information & Management

The purpose of this study was to cross-validate these shorter versions in two new populations.

Drug and Alcohol Dependence

It is feasible to cross-validate and explicate dissimilar diabetes simulation models using standardized patients.

Diabetes Research and Clinical Practice

Expert writing Tips

Best practice

In research papers, clearly state the methods and data used to "cross validate" your results to enhance transparency and credibility.

Common error

Avoid validating results using methods that are too similar or dependent on each other. This can lead to confirmation bias and weaken the validation process.

Linguistic Context

The phrase "cross validate" functions as a verb phrase, typically used in scientific and academic contexts. It indicates the act of verifying or confirming results using independent sources or methods, as Ludwig AI examples show.

Expression frequency: Uncommon

Frequent in

Science

65%

Science & Research

25%

Formal & Business

10%

Less common in

News & Media

0%

Wiki

0%

Academia

0%

Ludwig's WRAP-UP

The phrase "cross validate" is a verb phrase primarily used in scientific and research contexts to denote the verification of results using independent methods or datasets. As Ludwig AI confirms, it's grammatically correct and serves to enhance the credibility and reliability of findings. While not extremely common, its usage is consistent in formal, academic settings. To ensure accurate use, "cross validate" should involve distinct methods to avoid confirmation bias.

More alternative expressions

Phrases that express similar concepts, ordered by semantic similarity:

Verify independently

Emphasizes performing verification using separate and distinct methods or sources.

Confirm with multiple sources

Highlights the use of several sources to ensure the reliability of information.

Corroborate evidence

Focuses on strengthening evidence by finding supporting information from different origins.

Validate against other data

Implies validating data by comparing it against a different data set.

Double-check the results

Suggests a re-examination of results to ensure accuracy.

Authenticate using alternate methods

Refers to verifying authenticity through different methodologies.

Check for consistency

Highlights the importance of ensuring that different pieces of information are in agreement.

Substantiate through triangulation

Implies using multiple data points to confirm a finding from various perspectives.

Test for reliability

Focuses on assessing the trustworthiness of the information or results.

Verify the findings

Indicates the act of checking the correctness and truthfulness of research outcomes.

FAQs

What does "cross validate" mean in research?

In research, "cross validate" means to verify the results or findings of a study using an independent dataset or method. This helps ensure the robustness and generalizability of the results.

How do you "cross validate" a model?

To "cross validate" a model, you can split your dataset into training and validation sets. The model is trained on the training set and then tested on the validation set to assess its performance on unseen data. Techniques like k-fold cross-validation can also be employed.

What is "cross validation" and why is it important?

Cross validation is a technique used to assess how well the results of a statistical analysis will generalize to an independent data set. It is important because it helps to avoid overfitting and provides a more accurate estimate of the model's performance.

What are some alternatives to the phrase "cross validate"?

Alternatives to "cross validate" include "verify independently", "confirm with multiple sources", or "corroborate evidence". These phrases can be used interchangeably depending on the specific context.