Resampling methods or a single validation set work well for this purpose. To choose the best tuning parameter combination, each candidate set is assessed using data that were not used to train that model. 20.1 Creating the Training Set for Stacking.19 When Should You Trust Your Predictions?.18.4 Building Global Explanations from Local Explanations.17.3.1 Effect encodings with partial pooling.17.3 Using the Outcome for Encoding Predictors.16.5.4 Uniform manifold approximation and projection.16.2 A Picture Is Worth a Thousand… Beans.16.1 What Problems Can Dimensionality Reduction Solve?.15.1 Modeling Concrete Mixture Strength.14.3.1 Simulated annealing search process.13.4 Tools for Creating Tuning Specifications.12.5 Two general strategies for optimization.12.4 The consequences of poor parameter estimates.12.2 Tuning Parameters for Different Types of Models.12 Model Tuning and the Dangers of Overfitting.11.2 Comparing Resampled Performance Statistics.11.1 Creating Multiple Models with Workflow Sets.10.2.4 Rolling forecasting origin resampling.10 Resampling for Evaluating Performance.8.4.1 Encoding qualitative data in a numeric format.8.1 A Simple recipe() for the Ames Housing Data.7.5 Creating Multiple Workflows at Once.7.4.1 Special formulas and inline functions.7.4 How Does a workflow() Use the Formula?.7.3 Adding Raw Variables to the workflow().
7.1 Where Does the Model Begin and End?.5.4 Other Considerations for a Data Budget.
4.1 Exploring Features of Homes in Ames.
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