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Expanding your machine learning toolkit: Randomized search, computational budgets, and new algorithms
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Expanding your machine learning toolkit: Randomized search, computational budgets, and new algorithms

Introduction Previously, we wrote about some common trade-offs in machine learning and the importance of tuning models to your specific dataset. We demonstrated how to tune a random forest classifier using grid search, and how cross-validation can help avoid overfitting when tuning hyperparameters (HPs). In this follow-up post, you’ll beef up your machine learning toolbox … Continue reading