Normal case | XGBoost | LightGBM | Probabilistic classification | LightGBM (binary probabilistic)

Overview | Installation | Usage | Compact SHAP analysis | Decompose single predictions | SHAP Interactions | Interface to other packages | LightGBM | fastshap | shapr | H2O | treeshap | DALEX | kernelshap | Any other package | References

Overview | Installation | Usage | Performance | Permutation importance regarding first metric | ICE curves for Petal.Width | PDPs | 2D PDP | ALE | Different profile plots in one | Variable contribution breakdown for single observation | Global surrogate tree | Multiple models | flashlights | Typical predict_functions (a selection) | References

Overview | Example: Multiclass XGBoost model | Similar for LightGBM (only code) | Or for a random forest with | Example: SHAP subgroup analysis | Example: Different models | Fit linear regression and use {kernelshap} to get SHAP values

Setting | A first example | Two modifications | References

How to deal with censored variables? | Example | References

How to use {missRanger} for multiple imputation? | Example | References

Overview | Installation | Usage | Predictive mean matching | Controlling the random forests | Extended output | Formulas | Speed-up things | Trick: Use case.weights to reduce impact of rows with many missings | References

Overview | Usage | Simple validation | CV | Repeated CV | Stratification on multiple columns

Overview | Installation | Usage | References

Overview | Installation | Usage | Regression | Binary classification | Generalized R-squared | Pipe | Parametrized scoring functions | Murphy diagrams | References

Overview | Installation | Usage | Pipe | Out-of-sample application | The formula interface | Too many values are identified as outliers. Crap! | I don't want outliers to be replaced! | The algorithm takes too much time. What can I do? | References