harms modeling
Harms modeling is part of responsible AI and it is a method which can identify and mitigate risks or harms which may be caused by deploying AI solutions in production.
Category: AI and ML
Artificial Intelligence and Machine Learning
AI and ML
holdout
In machine learning, holdout validation is a data sampling method in which the dataset is split into two: the training dataset and the test. The split is equal, i.e. training is performed on the 50% of the dataset and testing is performed on the remaining 50% of the dataset. Holdout validation is not recommended in ... Read more
hyperparameter
In machine learning, a hyperparameter is a parameter external to the ML model, which controls the learning process. A hyperparameter is not related to the internal workings of the ML model but rather indirectly affects the model's internal parameters via the ML model training (fitting) process. The usage of hyperparameter types vary depending on the ... Read more