What is validation set

Machine learning is a popular method to develop intelligent systems that can learn from the data. It is a process in which the machine learns the patterns and rules from the data and applies these rules to make predictions or decisions. In machine learning, several techniques are used to train the model on the data, and validation is one of them.

Validation set is a portion of data used to evaluate the performance of a machine learning model trained on the training set. It is used to estimate the generalization error of the model. Validation set is an essential part of the machine learning process because it helps to evaluate the performance of the model before it is deployed in real-world scenarios.

Validation set can be created by splitting the data into three parts, i.e., training, validation, and testing. The training data is used to train the model, the validation data is used to evaluate the performance of the model during training, and the testing data is used to test the model’s performance after training.

Validation set is used to estimate the accuracy of the model on unseen data. By using a validation set, the machine learning model can learn the patterns and rules from the training data and avoid overfitting the model to the training data. Overfitting is a common problem in machine learning, and it occurs when the model is too complex and captures the noise in the training data.

Validation set is used to estimate the generalization error of the model. The generalization error is the error that occurs when the model is applied to new data. The purpose of machine learning is to create a model that can accurately predict the outcome for new data. Validation set helps to estimate the generalization error of the model, and by reducing this error, we can create a more accurate model.

To create a validation set, it is essential to use the correct technique and ensure that the validation set is representative of the entire dataset. The most popular technique to create a validation set is cross-validation. Cross-validation is a technique in which the data is divided into k-folds, and each fold is used as the validation set, and the remaining data is used as the training set. This process is repeated k times, and the average of the results is taken to estimate the generalization error of the model.

In conclusion, validation set is an important part of the machine learning process. It helps to estimate the generalization error of the model, which is critical in developing an accurate model. By using validation set, we can avoid overfitting the model to the training data and create a model that can accurately predict the outcome for new data.