Methods · Tabular
Classic / statistical fits via scikit-learn. Boosting prefers XGBoost → LightGBM → CatBoost → sklearn when installed. No custom Python required.
Prerequisitefamily: tabular in recipe.yaml · data.csv with a target column
method: linear | logistic | ridge | lasso | elasticnet
Linear models. Set lambda / l1_ratio as needed for ridge, lasso, elasticnet.
example
family: tabularmethod: ridgelambda: 1.0data: path: data.csv target: yeval: metric: msemethod: tree | forest | boosting | gp
Trees, random forests, gradient boosting, and gaussian processes. Boosting library: auto | xgboost | lightgbm | catboost | sklearn.
example
family: tabularmethod: boostinglibrary: autotrees: 100depth: 4data: path: data.csv target: yeval: metric: accuracy