10-fold cross validation vs ten-fold cross validation

Both "10-fold cross validation" and "ten-fold cross validation" are correct wordings. The choice between using the numerical value "10" or the written form "ten" is a matter of style and personal preference.

Last updated: April 01, 2024 • 99 views

10-fold cross validation

This phrase is correct and commonly used in the context of machine learning and data analysis.

This phrase refers to a technique used to evaluate the performance of a predictive model by dividing the data into 10 subsets or folds. Each fold is used as a testing set while the rest are used for training.

Examples:

The researchers used 10-fold cross validation to assess the accuracy of the machine learning model.

Show examples from the web [+]

The general approach to cross-validation in JMP Pro is to use a validation column.
In addition, it includes robust fitting options. Both the Partition and Neural platforms in JMP Pro take advantage of using cross-validation.
Using model comparison in JMP Pro, you can compare all the saved prediction columns from various fits and pick the best combination of goodness of fit, parsimony and cross-validation.
Grading methods shall be authorised only if the root mean squared error of prediction (RMSEP), computed by a full cross-validation technique or by a test set validation on a representative sample of at least 60 carcases, is less than 2,5.
Validity of results (case of principal axes methods), assessments of visualization techniques: classical inference, resampling techniques (bootstrap, partial bootstrap, total bootstrap, bootstrapping variables, cross-validation).
2. Grading methods shall be authorised only if the root mean squared error of prediction (RMSEP), computed by a full cross-validation technique, is less than 2,5. In addition, any outliers shall be included in the calculation of RMSEP. .

Alternatives:

Ten-fold cross validation

ten-fold cross validation

This phrase is correct and can be used interchangeably with "10-fold cross validation."

This phrase has the same meaning as "10-fold cross validation" and is commonly used in the context of machine learning and data analysis.

Examples:

The study employed ten-fold cross validation to validate the results of the algorithm.

Show examples from the web [+]

The general approach to cross-validation in JMP Pro is to use a validation column.
In addition, it includes robust fitting options. Both the Partition and Neural platforms in JMP Pro take advantage of using cross-validation.
Using model comparison in JMP Pro, you can compare all the saved prediction columns from various fits and pick the best combination of goodness of fit, parsimony and cross-validation.
Grading methods shall be authorised only if the root mean squared error of prediction (RMSEP), computed by a full cross-validation technique or by a test set validation on a representative sample of at least 60 carcases, is less than 2,5.
Validity of results (case of principal axes methods), assessments of visualization techniques: classical inference, resampling techniques (bootstrap, partial bootstrap, total bootstrap, bootstrapping variables, cross-validation).
2. Grading methods shall be authorised only if the root mean squared error of prediction (RMSEP), computed by a full cross-validation technique, is less than 2,5. In addition, any outliers shall be included in the calculation of RMSEP. .

Alternatives:

10-fold cross validation

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10-fold cross validation vs ten-fold cross validation