Glossary

Validation Set

A validation set is a data set used in machine learning to evaluate the performance of a predictive model. It is a subset of the overall data set that is held back from the training process and is used to test the model's accuracy and generalization ability.

During model training, the algorithm learns from the training data and adjusts its parameters to minimize the error on the training set. However, the goal of machine learning is not just to make accurate predictions on the training data but also to generalize well to new, unseen data. This is where the validation set comes in.

Once a model is trained on the training set, it is evaluated on the validation set to measure its performance on new, unseen data. If the model performs well on the validation set, it is an indication that it has learned to generalize well. On the other hand, if the model performs poorly on the validation set, it is an indication that it has overfit the training data and may not perform well on new data.

The size of the validation set is typically around 20% of the overall data set, although this can vary depending on the size of the data set and the specific problem being addressed. It is important to ensure that the validation set is representative of the overall data set and that the distribution of the target variable is similar in both the training and validation sets.

In summary, a validation set is a crucial tool in evaluating the performance and generalization ability of a machine learning model. By holding back a subset of the data for validation, we can ensure that our model learns to generalize well and performs well on new, unseen data.

A wide array of use-cases

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