Using advanced techniques for model evaluation and selection in MATLAB
Model evaluation and selection are crucial steps in machine learning to ensure the chosen model performs well on unseen data and generalizes effectively. When it comes to advanced techniques for model evaluation and selection in MATLAB, there are several approaches you can consider.
In the subsequent sub-section, we will take a look at the most important techniques for model evaluation and selection.
Understanding k-fold cross-validation
K-fold cross-validation is a widely used technique for model evaluation and selection. It involves partitioning the dataset into k equally sized subsets or folds. The model undergoes training and assessment in k iterations, with each iteration employing a distinct fold as the validation set while using the remaining folds as the training set. The outcomes of each iteration are then averaged to derive a comprehensive performance estimation. This is the essence of how k-fold...
