Subject
3 entries
Cross Validation
Bookmarks
Scikit-Learn: Model Validation and Testing (PyCon 2013 Notebook)
Jake VanderPlas's PyCon 2013 notebook on model validation and testing in scikit-learn — covers train/test splits, cross-validation, and model selection in executable notebook form. A practical tutorial that shaped how Python practitioners learned to evaluate models.
How To Choose The Right Test Options When Evaluating Machine Learning Algorithms
Jason Brownlee's guide to choosing between hold-out validation, k-fold cross-validation, and bootstrap estimation when evaluating ML algorithms. Covers when each approach is appropriate given dataset size and computational budget.
Design and Run Your First Experiment in Weka
Jason Brownlee's Machine Learning Mastery guide to designing and running experiments in Weka — the GUI-based ML tool from Waikato. Shows how to set up proper comparative experiments with statistical testing, not just running one algorithm.