Validation set
- See also: Machine learning terms
=Introduction
Machine learning aims to construct predictive models that can accurately forecast new and unseen data. Training a machine learning model involves teaching it labeled data so it can learn patterns and relationships within it; however, after training the model, evaluation of its performance on unlabeled datasets must take place - this is where validation sets come into play.
What is a validation set?
Validation sets are datasets held back during model training to evaluate its performance. After training on the training set, this validation set helps assess how well the model generalizes to newly unseen data. It serves to fine-tune model hyperparameters and prevent overfitting.
Why is a validation set important?
Validation sets are used to assess the performance of a model on data it has never encountered before. They ensure that the model does not overfit from training data, which can lead to poor performance when presented with new information. By testing on a validation set, we can adjust model hyperparameters so it does not overfit and performs well on new information.
How is a validation set created?
Create a validation set, in which part of the labeled dataset is held back before model training starts. The remaining data then goes towards training the model. This validation set should be representative of actual world data encountered by the model; typically 20-30% of total dataset size should go into it; however, depending on size and problem being solved, this number may differ.
How is a validation set used?
After the model has been trained on a training set, it is evaluated on the validation set to assess its performance. This validation set allows us to tune hyperparameters of the model - parameters not learned during training such as learning rate or hidden layer count in neural networks - based on its performance on this validation set. By altering these hyperparameters according to new information that cannot be seen during training, we can improve its capability when faced with new data sets.
Explain Like I'm 5 (ELI5)
Machine learning aims to build a model that can accurately predict data it has never seen before. To guarantee this, we utilize a validation set - part of our data set we keep hidden from the model while teaching it how to make predictions. Once trained, we test its predictions against this validation set to see if improvements need to be made for improved accuracy. If necessary, these changes can be made to make the model even better at making forecasts on new data sets.
Explain Like I'm 5 (ELI5)
Imagine you have a basket of apples and you need to sort them into two categories: red apples and green apples. Although you've seen plenty of apples before, how do you know which ones are red and which are green?
So, you decide to practice by taking some apples out of the basket and labeling them either red or green - these will serve as your "validation set".
Use this validation set as a practice run for sorting apples into correct groups. Once you feel confident with your sorting abilities, you can move on to sorting all of the apples in your basket.
A validation set helps us confirm that our model is functioning as expected, just as machine learning requires us to ensure our model works correctly. We utilize a validation set to test our model and confirm it accurately predicts what we want it to predict.