👉 Holdouts are a method used in machine learning to prevent overfitting, where the model performs well on training data but poorly on new or unseen test data. This is often done by selecting a subset of the training data that is not used during training and using it to train the model until it has learned from the training data. In other words, holdouts are used to evaluate the performance of a machine learning model on new data and determine if it's overfitting or not. The idea
Outrageously Funny Search Suggestion Engine :: Holdout
What is the definition of Holdout? 🙋
👉 Holdout is a technique in machine learning where you use a subset of your training data to train and test your model. This approach helps to prevent overfitting, where the model performs well on the majority of data but not as well on minority cases. In simple terms, it means that you take a small portion of your dataset, say 50% (or any percentage you prefer), and use it to train your machine learning model. This is usually done in a way that doesn't
