Your English writing platform
Discover LudwigExact(1)
To address this, we have chosen a validation scheme to train SVM models on only one donor, and then validate on the other.
Similar(59)
The minimized signatures were built on the training dataset and be validated on the validation dataset.
The model with the best performance was validated on the validation set.
The local recurrence profile and its associated threshold value were subsequently validated on the validation set.
The multiple imputation strategy developed was then validated on the 30% validation sample.
Correspondingly, the swap models are signatures built on validation dataset and be validated on the training dataset.
Therefore, we applied this analysis on one subset (the training set), and validated on the other independent subset (the testing set).
In the cross-donor experiment, training data were drawn entirely from one donor and the resulting SVMs were validated on the entire dataset from the other donor.
Every logistic regression model was validated on the training dataset with 100-fold bootstrapped cross-validation (Efron & Tibshirani, 1993).
The results were validated on the SIMONA Research Simulator (SRS) at TU Delft.
Our approach is empirically validated on the iCub robot.
Write better and faster with AI suggestions while staying true to your unique style.
Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com