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Jesus and the Cross - Biblical Archaeology Society Throughout the world, images of the cross adorn the walls and steeples of churches For some Christians, the cross is part of their daily attire worn around their necks Sometimes the cross even adorns the body of a Christian in permanent ink In Egypt, among other countries, for example, Christians wear a tattoo of the cross on their wrists And for some Christians, each year during the
How Was Jesus Crucified? - Biblical Archaeology Society Gospel accounts of Jesus’s execution do not specify how exactly Jesus was secured to the cross Yet in Christian tradition, Jesus had his palms and feet pierced with nails Even though Roman execution methods did include crucifixion with nails, some scholars believe this method only developed after Jesus’s lifetime
The Staurogram - Biblical Archaeology Society When did Christians start to depict images of Jesus on the cross? Larry Hurtado highlights an early Christian staurogram that sets the date back by 150–200 years
Ancient Crucifixion Images - Biblical Archaeology Society This second-century graffito of a Roman crucifixion from Puteoli, Italy, is one of a few ancient crucifixion images that offer a first-hand glimpse of Roman crucifixion methods and what Jesus’ crucifixion may have looked like to a bystander
Correct method to report Randomized Search CV results This is because RandomizedSearchCV does cross-validation, so the second method would be the right way, since we aren't doing any cross validation for the "reported" metrics (accuracy, AUC, etc ) except for recall with the first method I don' know if my understanding about this is correct
Does modeling with Random Forests require cross-validation? Even Trevor Hastie, in a relatively recent talks says that "Random Forests provide free cross-validation" Intuitively, this makes sense to me, if training and trying to improve a RF-based model on one dataset Can someone please lay out the arguments for and against the need for cross-validation with random forests?
Nested-cross validation pipeline and confidence intervals Introduction Training a binary classifier on a small and imbalanced dataset (220 samples, 58 positives) poses some challenges in ensuring robust model evaluation and generalisation This response will address: Correctness of your nested CV pipeline Appropriateness of bootstrap confidence intervals for performance metrics Implications of feature selection within folds Additional