In Machine Learning, we use a model to learn patterns from a dataset so that it can make predictions on new, unseen data. However, we need to evaluate the performance of the model before deploying it in the real world. This is where the concept of training set, validation set and test set come into picture.
A training set is a subset of the given dataset that is used to train the model. Typically, the larger the training set, the better it is for the model to learn the patterns in the data. During the training phase, the model uses the input data and their corresponding expected output to learn the underlying patterns in the data that can be used to make predictions on new data.
Once the model is trained, we use a validation set to tune the hyperparameters of the model so that it can generalize better to new data. A validation set is a subset of the training data that is not used during the training phase but used to tune the hyperparameters of the model. Hyperparameters are model attributes that are set prior to training and can be adjusted to improve the performance of the model. By using a validation set, we can select the best hyperparameters for the model which will give us the best performance on the test data.
Finally, a test set is a subset of the data that the model has never seen before, and it is used to evaluate the performance of the model after it is trained and tuned on the training and validation set. The test set is used as a proxy for the real-world performance of the model, and it gives us an estimate of the model’s accuracy when making predictions on new and unseen data.
The importance of using training, validation, and test sets lies in ensuring the success of the machine learning model when deployed in the real world. By using these sets, we can train and fine-tune the model to achieve high accuracy while ensuring that the model can generalize well to new and unseen data. Moreover, it prevents the problem of overfitting, which occurs when the model is trained to fit the training set too closely, thereby leading to poor performance on new data. By using separate datasets for training, validation, and testing, we can accurately evaluate the performance of the model and make the necessary modifications to enhance its performance.