Overview | Required packages | Step 1: Prepare data | Step 2: Define model specifications | Step 3: Create a workflow set | Step 4: Compute boundaries for all models | Step 5: Plot with facets | Step 6: Disagreement map | Using pre-trained workflows

Overview | Workflow overview | Step 1: Prepare data | Step 2: Fit models on the same data | Step 3: Generate one shared projection sequence | Step 4: Visualize at one tour frame | Step 5: Compare models at the same frame | Animating the tour (optional) | Key points

The prediction philosophy | 1. Standard classifiers (no extra work needed) | 2. Non-standard models (using predfun) | 3. Officially supported classifiers (built-in adapters) | The adapter contract | 4. Writing a custom S3 adapter

What is classbound? | Installation | Quick start: one-step boundary plot | The modular API | Using different classifiers | Probability surface | Interactive exploration | What's next?

The problem with more than two features | Setup | 2D Slice | Projection | When to use each strategy | High-dimensional data: data69_1

Launching Explorapp | Data modes | Import Data | Simulate Data | Draw Data | Model configuration | Visualization modes | Navigate | 2D Slice | Projection | Probability surface | Outlier injection | Performance metrics | Export | Import Workspace Models

Introduction | Projection pursuit classification forest | Overview PPforest package | Example | REFERENCES