Validation Techniques

Validation techniques are methods used to estimate how well a machine learning model will generalize to unseen data. They involve splitting the available data into different subsets: a training set used to train the model, and a validation set used to evaluate the model's performance during training or hyperparameter tuning. By assessing the model on data it hasn't seen before, validation techniques help to identify issues like overfitting and underfitting, and ultimately guide the selection of the best model for a given task.

Visit the following resources to learn more:

• @article@Cross-validation: evaluating estimator performance | scikit-learn
• @article@The 5 Stages of Machine Learning Validation
• @article@What is the Difference Between Test and Validation Datasets?
• @video@Validating Machine Learning Model and Avoiding Common Challenges