The Role of Validation Sets in Model Training | Train-Test-Validation Splits | Clearly explained!

Key Takeaways

in our previous video we talked about the train test split why is it important and how to properly split the data set in this video we will refer to validation data a proportion from the overall data set that has a significant role machine learning model creation has the following steps firstly splitting the data into train validation and test sets secondly doing data analysis and feature engineering thirdly training models hyperparameter tuning and model selection and lastly final model testing training and validation sets are used for training and preparing the final model while the test set is only used for final evaluation validation data can be defined as a set that is not used for training but we use its results during the training process to select the appropriate model and configure its hyperparameters so while it can be referred to as unseen data we still used its information to select our final model eval Val ating the final model only on the validation set will provide an optimistic estimation of the performance in other words we already maximize the performance of the final model based on the validation data during the training process training performance and validation performance are evaluated to see how the performance of the model is improving if we see that both sets are improving then we are on the correct path the validation set ensures that the model does not just memorize the training data but learns patterns that apply to new data as well remember that at some point the training performance will continue to improve but we will start seeing a declining performance on the validation set this is the point that suggests we stop the training because it starts to overfit a concept we will refer to in the upcoming videos as a result you will select the model and the set of hyperparameters that provide the highest validation performance finally you evaluate the unbiased performance estimate using the test data the size of the validation set is often similar to the test size sometimes a bit less just be sure to have data large enough to provide a reliable estimate of the model's performance if you want to learn more about artificial intelligence subscribe to our channel to be aware of the new videos press the like button and let's discuss AI in the comments section