Fitting a regression forest | Inside a tree family | Decomposing predictions | Classification | Binary | Multiclass | Using recipes | Saving and restoring models

Core Concepts | Basic Example | Permutation Feature Importance (PFI) | Leave-One-Covariate-Out (LOCO) | Feature Samplers | Detailed Scoring Information | Observation-wise losses and importances | Statistical Inference | Using Pre-trained Learners | Parallelization | Example with future | Example with mirai

1. Find things | Finding Things (and the right amount) | Item identifiers | Extended Information:

Step 1: Search | Step 2: Use the ID | Step 3: Tidying up | Step 4: Graph!

Search | Movies | Shows | Seasons | Episodes | People | Users | Comments | Lists | Recommendations | Misc

Setup | Base Class: FeatureSampler | Key Properties | Permutation Sampler | Marginal Reference Sampler | Conditional Samplers | Gaussian Conditional Sampler | ARF Sampler | Ctree Conditional Sampler | kNN Conditional Sampler | Example 1: All-numeric conditioning (Euclidean distance) | Example 2: Mixed-type conditioning (Gower distance) | Knockoff Samplers | Gaussian Knockoffs | Summary

Introduction | Overview of Simulation Settings | 1. Correlated Features DGP | Mathematical Model | Causal Structure | Usage Example | 2. Mediated Effects DGP | 3. Confounding DGP | 4. Interaction Effects DGP | 5. Independent Features DGP (Baseline) | Expected Behavior | 6. Ewald et al. (2024) DGP