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Discover LudwigThe word 'cross-validated' is a correct and usable term in written English
It is commonly used in the field of data analysis and machine learning to refer to a method of evaluating the performance of a predictive model. 'Cross-validated' means that the model has been tested on multiple subsets of the data, in order to ensure that the results are not biased towards a particular set of data. An example of its usage could be: "The accuracy of our machine learning model was cross-validated using a 10-fold cross-validation technique, resulting in a more reliable evaluation of its performance."
Exact(55)
The classifier results are cross-validated using hold out cross-validation.
Model significance was assessed using a cross-validated ANOVA based on seven-fold cross validation.
cross-validated determination coefficient.
Leave-one-out cross-validated regression coefficient.
CoMSIA hydrophobic cross-validated q2 was 0.37 and the non-cross-validated r2 was 0.85.
Additionally, trained models are cross-validated to examine overfitting.
Similar(5)
non-cross-validated correlation coefficient.
The obtained results were cross validated using chromatographic method HPLC.
The method was parallel cross validated with UV visible spectroscopy.
The corresponding non-cross-validated analysis gave sFIT = 0.527 and r2 = 0.687.
Data sets will be cross validated and errors corrected.
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