What is validation set?
A subset of data used to evaluate the performance of a machine learning model
validation set explained in plain English
A validation set is a portion of data that is held back from the training process and used to test how well a machine learning model is performing. It helps to prevent overfitting and gives an idea of how the model will perform on unseen data.
Analogy
A validation set is like a practice exam that a student takes before the final exam. It helps the student to identify areas where they need to improve and gives them an idea of how they will perform on the final exam.
Example
A company building a machine learning model to predict customer churn might use a validation set to test the model's performance. The validation set would consist of a subset of customer data that is not used in the training process, and the model's performance on this set would give an idea of how well it will perform on new, unseen data.
How is validation set used?
The validation set is used to evaluate the performance of a machine learning model during the training process. The model is trained on a training set, and then its performance is evaluated on the validation set. The results are used to adjust the model's parameters and improve its performance.
Common misconceptions about validation set
One common misconception is that the validation set is the same as the test set. However, the validation set is used during the training process to evaluate and improve the model, while the test set is used after the model is trained to evaluate its final performance.
History
The concept of a validation set has been around since the early days of machine learning. It was first used as a way to prevent overfitting and improve the performance of machine learning models.
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