Examples of 'cross-validation' in a sentence
Meaning of "cross-validation"
cross-validation (noun) - In the field of statistics and machine learning, cross-validation is a technique used for assessing the performance and generalizability of a predictive model. It involves partitioning the data into subsets, training the model on some subsets, and testing it on others
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- Any technique or instance of assessing how the results of a statistical analysis will generalize to an independent dataset.
How to use "cross-validation" in a sentence
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cross-validation
Cross-validation of different prevalence estimation methods.
In some cases a cross-validation approach is applied.
The amount of shrinkage is determined by cross-validation.
We also investigated cross-validation to select a potentially.
The corresponding tuning is performed by cross-validation.
Cross-validation is a way to estimate the size of this effect.
This practice is referred to as cross-validation in statistics.
Cross-validation is a statistical method for validating a predictive model.
Evaluate the model using cross-validation.
Cross-validation is often used to estimate this generalization performance.
The generalisation performance is evaluated by cross-validation.
Cross-validation is a more sophisticated version of training a test set.
We will not be doing well on the cross-validation set.
We used the cross-validation method to search the optimal order.
O fits to observational data by minimizing generalized cross-validation.
See also
And this is what this cross-validation procedure is for.
Cross-validation provides a good indicator of predictive performance on unknown fuels.
Specific attention paid to cross-validation.
Cross-validation of bioelectrical impedance analysis of body composition in children and adolescents.
This value is usually tuned using cross-validation.
This choice stemming from the cross-validation method is asymptotically optimal.
The third step involves consistency checking and cross-validation.
Monitoring nonfatal overdoses can allow cross-validation of information on drugrelated deaths.
The most common approach used in this context is cross-validation.
Cross-validation studies were carried out to check model adequacy for other populations.
This is generally achieved by carrying out a cross-validation analysis.
Cross-validation can be used to compare the performances of different predictive modeling procedures.
I am going to abbreviate cross-validation.
A cross-validation method that takes into account the time of observations is used.
I am going to pick the hypothesis with the lowest cross-validation error.
In this study, the process of cross-validation was different than the prior ones.
One method for choosing when to stop training is cross-validation.
The same training and cross-validation sets were used throughout this study.
Alternative methods of controlling overfitting not involving regularization include cross-validation.
However there are many ways that cross-validation can be misused.
In addition, the number of principal components included in the model is determined by cross-validation.
This often involves cross-validation with training and test data sets.
The output of the model was corrected for potential bias vía cross-validation.
Besides, the question of model selection via cross-validation is considered through two approaches.
And this is testing the data that comes out of the cross-validation.
The double cross-validation approach was used to assess the predictive ability of the models.
The choice of bandwidth is mainly done through cross-validation and excess of zeros.
Cross-validation is a popular technique you can use to evaluate and validate your model.
Omits one observation or group of observations, depending on the cross-validation method.
The cross-validation is performed by using one spectra chosen at random to test the model.
The second approach is related to the interpretation of cross-validation in terms of penalized criterion.
The cross-validation was repeated using blocks of two spectra at a time to test the model.
All models were successfully validated using the cross-validation method described above.
Besides this, we use cross-validation to estimate the statistical performance of the decision tree.
The weights used for combination have been calculated using cross-validation tests.
