Introduction | Fuel-economy data | 70 / 30 train–test split | Helper metrics | Baseline: linear regression | Model 1: ε-SVR with MAPE | Model 3: LS-SVR with RMSPE | Comparing objectives | Using psvr with tidymodels | Hyperparameter search ranges | rbf_sigma is a length scale, not a precision | rbf_sigma_psvr_data() needs preprocessed predictors | LS-SVR needs cost_psvr_ls_data(), and it cannot be automated | cost is not comparable across datasets either | Tuning sym_type cannot answer "does symmetry help" | The outcome stays in original units | References

Data | 1 — Split | 2 — Preprocessing recipe | 3 — Model spec with tune() | 4 — Workflow | 5 — Tune with 5-fold CV | 6 — Select best | 7 — Final fit and test-set evaluation | 8 — Inspecting the fitted psvr model

Introduction | Fuel-economy data | 70 / 30 train–test split | Helper metrics | Baseline: linear regression | Model 1: ε-SVR with MAPE | Model 3: LS-SVR with RMSPE | Comparing objectives | Using psvr with tidymodels | Hyperparameter search ranges | rbf_sigma is a length scale, not a precision | rbf_sigma_psvr_data() needs preprocessed predictors | LS-SVR needs cost_psvr_ls_data(), and it cannot be automated | cost is not comparable across datasets either | Tuning sym_type cannot answer "does symmetry help" | The outcome stays in original units | References

Data | 1 — Split | 2 — Preprocessing recipe | 3 — Model spec with tune() | 4 — Workflow | 5 — Tune with 5-fold CV | 6 — Select best | 7 — Final fit and test-set evaluation | 8 — Inspecting the fitted psvr model