Concepts of evaluating and validating training looking for secret crush dating service
It’s clear that learning and development professionals are struggling to answer this question – a question we simply can’t afford to ignore any longer. With so much effort devoted to training, the real question becomes evident: Did anyone really learn?Quality cannot be adequately assured merely by in-process and finished product inspection or testing so the firms should employ objective measures (e.g.validation) wherever feasible and meaningful to achieve adequate assurance.Note on Cross Validation: Many a times, people first split their dataset into 2 — Train and Test.After this, they keep aside the Test set, and randomly choose X% of their Train dataset to be the actual set, where X is a fixed number(say 80%), the model is then iteratively trained and validated on these different sets.
The evaluation becomes more biased as skill on the validation dataset is incorporated into the model configuration.
With the rapidly growing need to get employees educated and running at peak performance, organizations need to focus on other ways to measure learning is taking place.
This will allow them to focus their time, energy and resources on training initiatives that move the needle.
The test set is generally what is used to evaluate competing models (For example on many Kaggle competitions, the validation set is released initially along with the training set and the actual test set is only released when the competition is about to close, and it is the result of the the model on the Test set that decides the winner).
Many a times the validation set is used as the test set, but it is not good practice. It contains carefully sampled data that spans the various classes that the model would face, when used in the real world.