In machine learning, a validation set is a subset of the available labeled data that is used to assess the performance of a trained model during the training phase. The primary purpose of the validation set is to provide an independent evaluation of the model's ability to generalize to new, unseen data. The typical data split in machine learning involves three main subsets:

Training Set:

The largest portion of the dataset is used to train the machine learning model. The model learns patterns, relationships, and features within this set.

Validation Set:

A separate portion of the dataset, not used during training, is set aside for validation. After training the model on the training set, it is evaluated on the validation set to assess its generalization performance.

Test Set:

Another distinct subset of the data, also not used during training or validation, is reserved for final evaluation. The test set provides an unbiased estimate of the model's performance and is used to assess how well it is expected to perform on new, unseen data.

The validation set plays a crucial role in the machine learning pipeline for the following reasons:

• Hyperparameter Tuning: During the training process, hyperparameters (settings not learned from data, such as learning rate or regularization strength) are tuned to optimize performance. The validation set helps in choosing the best set of hyperparameters that yield good generalization.
• Early Stopping: Monitoring the performance on the validation set allows for early stopping, i.e., halting the training process when the model's performance on the validation set stops improving. This helps prevent overfitting, where the model becomes too specialized to the training data and performs poorly on new data.
• Model Selection: If multiple models or algorithms are being considered, the validation set can be used to compare their performances and select the best-performing one.

The validation set provides an unbiased assessment of the model's ability to generalize by simulating its performance on new, unseen data. It helps machine learning practitioners make informed decisions about model architecture, hyperparameters, and other aspects of the model to achieve better generalization and avoid overfitting.