# Run Parameters

STREAMLINE parameters can be supplied through `.cfg` files, notebooks, or phase
CLI flags. The recommended full-pipeline path is the config runner:

```bash
python run.py -c run_configs/local/uci_binary_hcc.cfg --dry_run
python run.py -c run_configs/local/uci_binary_hcc.cfg
```

The `.cfg` parameter names intentionally match the command-line names wherever
possible. This page has two layers: a short set of essential parameters that
most users need to set correctly, followed by a more exhaustive phase-by-phase
reference. Essential parameters are repeated in the exhaustive reference so the
reader can either skim from the top or look up a single phase later.

Use these terms consistently when reading the tables:

* **Task type** means the supervised learning problem: `Binary`, `Multiclass`,
  or `Continuous`. This is controlled by `outcome_type`.
* **Execution mode** means where jobs run: `Serial`, `Parallel`, `Local`,
  `BashSLURM`, `BashLSF`, or a named Dask cluster. This is controlled by
  `run_cluster`.
* **Run path** means how STREAMLINE is launched: a notebook, the full config
  runner, or an individual phase CLI.
* **Report mode** means whether P11 creates a standard training/CV report or a
  replication report.

Later phases can load values saved by earlier phases in `metadata.pickle` and
`run_commands.pickle`. When a default below says it comes from metadata, the
owning phase is named where possible. For example, outcome labels, feature
types, CV counts, and one-hot settings are saved by P1; imputation, scaling,
and SMOTE settings are saved by P2; feature-learning settings are saved by P3;
feature-importance settings are saved by P4; modeling settings are saved by P6.
Explicit `.cfg` or CLI values override remembered values.

## Essential Parameters To Run STREAMLINE

These are the parameters most users should understand before starting a run.
Start from one of the included files in `run_configs/local/` or
`run_configs/hpc/`, change these values, and use `--dry_run` to inspect the
resolved phase calls before launching a full analysis.

### Every Config Run

These parameters define the run itself. They belong in `[run]` for config files,
or must be repeated on each individual phase CLI when running phases manually.

| Parameter | Default value | Where to set it | Description |
| --- | --- | --- | --- |
| `output_path` | Required | `[run]`; every phase CLI | Parent folder where STREAMLINE writes the experiment output. |
| `experiment_name` | Required | `[run]`; every phase CLI | Name of the experiment folder created under `output_path`. |
| `outcome_label` | `Class` | `[run]`, `[p1]`; repeated by later phase CLIs when needed | Outcome column in the input data. P1 records it for later phases. |
| `outcome_type` | P1 can infer; later phases should be explicit | `[run]`, `[p1]`, `[p6]`, `[p8]`, `[p9]`, `[p11]` | Task type: `Binary`, `Multiclass`, or `Continuous`. Set this explicitly for paper or benchmark runs. |
| `instance_label` | `None` | `[run]`, `[p1]`; repeated by later phase CLIs when needed | Optional row identifier column. P1 records it and excludes it from modeling. |
| `n_splits` | `10` | `[run]`; CV-aware phase CLIs | Number of cross-validation folds. Demo configs use `3` for speed. |
| `run_cluster` | `Serial` | `[run]`; every phase CLI | Execution mode. Use `Parallel` for local joblib multiprocessing, `Local` for local Dask, or `BashSLURM`/`BashLSF` for scheduler submission. |
| `random_state` | Phase-specific, often `None` or `0` | `[run]`; stochastic phase CLIs | Seed for reproducible CV partitioning, imputation/SMOTE, feature learning, modeling, and ensembles. |
| `phase_order` | `p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11` | `[phases]` | Ordered list of phases for the config runner. |
| `do_p1` through `do_p11` | `True` unless disabled | `[phases]` | Phase toggles. P10 also requires replication paths; P7 is skipped for continuous outcomes. |

### Phase-Specific Essentials

These parameters decide what data are analyzed, how features are handled, which
models run, and which reports are produced. They are grouped by the phase that
first uses or remembers them.

| Parameter | Default value | Where to set it | Description |
| --- | --- | --- | --- |
| `data_path` | Required for P1 | `[p1]` | Folder containing one or more input `.csv`, `.tsv`, or `.txt` datasets. |
| `categorical_features` | `None` | `[p1]` | Optional file listing categorical feature names. Recommended when feature types matter. |
| `quantitative_features` | `None` | `[p1]` | Optional file listing quantitative feature names. Recommended with `categorical_features`. |
| `ignore_features` | `None` | `[p1]` | Optional file or list of feature names to exclude before modeling. |
| `partition_method` | `Stratified` | `[p1]` | CV strategy. Continuous outcomes are forced to `Random`. |
| `one_hot_encoding` | `True` | `[p1]` | Expand non-binary categorical features in P1. If `False`, P6 only allows native-categorical models unless that guard is disabled. |
| `scale_data` | P2 remembered value, fallback `True` | `[p2]`, `[p8]` | Applies scaling in P2 and records whether scaled data was used in summary/reporting. |
| `impute_data` | P2 remembered value, fallback `True` | `[p2]` | Enables missing-value imputation for CV train/test folds. |
| `smote` | P2 remembered value, fallback `False` | `[p2]` | Enables classification-only training-fold oversampling after imputation and scaling. |
| `models` | All available non-excluded P6 models | `[p6]` | Model IDs to train, such as `NB,LR,DT,RF,CGB,HEROS,ExSTraCS`. Demo configs use small model lists for speed. |
| `model_params_json` | `None` | `[p6]` | Optional JSON or Python-literal dictionary of model-specific overrides. See [Model Parameter JSON](model_params_json.md). |
| `scoring_metric` | `balanced_accuracy` | `[p6]`, `[p8]` | Primary modeling/evaluation metric. Use `explained_variance` for regression unless intentionally changing the regression metric. |
| `metric_direction` | `maximize` | `[p6]` | Optuna optimization direction. Use `minimize` only for loss/error metrics where lower is better. |
| `n_trials` | `200` | `[p6]` | Maximum Optuna trials per model/CV job. |
| `timeout` | `900` | `[p6]` | Maximum Optuna time budget in seconds per model/CV job. |
| `training_subsample` | `0` | `[p6]` | Optional cap on training rows for models that explicitly allow subsampling. `0` disables it. |
| `skip_completed_models` | `False` | `[p6]` | When `True`, P6 runs only missing or failed model/CV jobs. Default behavior reruns requested model jobs and overwrites artifacts. |
| `rep_data_path` | Required for P10 | `[p10]` | Folder containing replication/external-validation datasets. |
| `dataset_for_rep` | Required for P10 | `[p10]` | Original training dataset path used to identify the trained dataset output folder. |
| `report_modes` | `standard` for P11; demo configs use `standard,replication` | `[p11]` | Report types generated by the config runner. |

CLI-only controls are prefixed with `--` in the exhaustive reference. They are
not written into `.cfg` files. Use them to choose a config file, dry-run a
config, run only part of the phase order, list registry methods, or control
saved run-command reuse.

### Config Template Folders

The `run_configs/` directory is organized by execution environment:

| Folder | Use case | Included examples |
| --- | --- | --- |
| `run_configs/local/` | Local serial, local joblib `Parallel`, and local Dask `Local` runs. | Binary HCC, multiclass student dropout, and regression Auto MPG demo configs. |
| `run_configs/hpc/` | Scheduler-oriented templates that use `BashSLURM`, `BashLSF`, or site-specific cluster settings. | `cedars_slurm_hcc.cfg` for Cedars/SLURM and `upenn_lsf_hcc.cfg` for UPenn/LSF starting points. |

The original top-level demo configs are still kept for backward compatibility,
but new examples should point users to the environment-specific subfolders.

For cluster-specific workflow details, including `tmux`, SLURM/LSF monitoring,
and recovery after interrupted runs, see [HPC and Cluster Runs](hpc.md).

## Full Parameter Reference By Phase

Defaults below are the current runner defaults when a parameter is omitted.
Where noted, later phases may load the value from experiment metadata saved by
earlier phases.

### Shared Run Parameters

| Parameter | Default value | Description |
| --- | --- | --- |
| `output_path` | Required | Parent folder for experiment outputs. |
| `experiment_name` | Required | Experiment folder name under `output_path`. |
| `outcome_label` | `Class` | Outcome column. Passed to P1 and reused by modeling, evaluation, comparison, replication, and reporting. |
| `outcome_type` | `None` in P1; later phases use metadata when possible | Learning task type: `Binary`, `Multiclass`, or `Continuous`. |
| `instance_label` | `None` | Optional row identifier column. |
| `n_splits` | `10` | Number of CV folds for CV-aware phases. |
| `run_cluster` | `Serial` | Execution mode. `Parallel` uses local joblib multiprocessing; `Local` uses a local Dask cluster; `BashSLURM` and `BashLSF` submit scheduler scripts. |
| `queue` | `defq` | Scheduler queue/partition for BashSLURM, BashLSF, or named cluster execution. |
| `reserved_memory` | `4` | Memory request in GB for submitted cluster jobs. |
| `random_state` | `None` in P1 and P6; metadata or `0` in several later phases | Seed for stochastic steps. Set explicitly for reproducible paper runs. |
| `wait_for_cluster_completion` | `True` for BashSLURM/BashLSF config runs | Makes the config runner wait for submitted cluster jobs to write completion markers before starting the next phase. |
| `cluster_phase_timeout` | `86400` | Maximum seconds to wait for a submitted cluster phase. |
| `cluster_phase_poll_interval` | `30` | Seconds between completion-marker checks for submitted cluster phases. |

### Config Runner Controls

These are command-line controls for `python run.py -c ...`, not `.cfg` keys.

| Parameter | Default value | Description |
| --- | --- | --- |
| `--config`, `-c` | Required | Path to a STREAMLINE `.cfg` or `.ini` file. |
| `--dry_run` | `False` | Print resolved phase runner calls without running phases. |
| `--start_at` | `None` | Start at a phase alias such as `p4` or `p6_modeling`. |
| `--stop_after` | `None` | Stop after a phase alias such as `p8` or `p11`. |
| `--only` | `None` | Run only a comma-separated set of phase aliases. |
| `--skip` | `None` | Skip a comma-separated set of phase aliases. |
| `--log_level` | `INFO` | Python logging level for the config runner. |

### Phase Toggles

| Parameter | Default value | Description |
| --- | --- | --- |
| `phase_order` | `p1,p2,p3,p4,p5,p6,p7,p8,p9,p10,p11` | Phase order used by the config runner. |
| `do_p1` | `True` | Run P1 data exploration and processing. |
| `do_p2` | `True` | Run P2 imputation, scaling, and optional SMOTE. |
| `do_p3` | `True` | Run P3 feature learning. |
| `do_p4` | `True` | Run P4 feature importance. |
| `do_p5` | `True` | Run P5 feature selection. |
| `do_p6` | `True` | Run P6 modeling. |
| `do_p7` | `True` | Run P7 ensembles. P7 is skipped automatically for continuous outcomes. |
| `do_p8` | `True` | Run P8 summary statistics and plots. |
| `do_p9` | `True` | Run P9 dataset comparison. P9 skips itself when fewer than two datasets are available. |
| `do_p10` | `True` when replication paths are configured | Run P10 replication or external validation. |
| `do_p11` | `True` | Run P11 reporting. |
| `enabled` | `True` | Per-phase override for disabling an individual phase section, commonly used as `enabled = False` in `[p7]` for regression configs. |
| `do_all` | Not set | Old-style broad toggle that enables or disables all phases when present. |
| `do_till_report` | Not set | Old-style broad toggle for running phases through the standard report path. |

### P1 Data Process

| Parameter | Default value | Description |
| --- | --- | --- |
| `data_path` | Required | Folder containing raw input datasets, or omitted only when importing prebuilt CV datasets. |
| `exclude_eda_output` | `None` | Optional list of EDA outputs to skip, such as `describe_csv` or `correlation`. |
| `match_label` | `None` | Optional column label used when matching or harmonizing datasets. |
| `ignore_features` | `None` | Optional file or list of feature names to exclude. |
| `categorical_features` | `None` | Optional feature-name file for categorical variables. |
| `quantitative_features` | `None` | Optional feature-name file for quantitative variables. |
| `top_features` | `20` | Number of top features shown in applicable P1 summaries. |
| `categorical_cutoff` | `10` | If feature-type files are absent, features with at most this many unique values may be treated as categorical. |
| `sig_cutoff` | `0.05` | Statistical significance threshold used in P1 analyses. |
| `featureeng_missingness` | `0.5` | Missingness threshold for creating missingness indicator features. |
| `cleaning_missingness` | `0.5` | Missingness threshold for removing high-missingness features or instances. |
| `correlation_removal_threshold` | `1.0` | Correlation threshold for removing highly correlated features. `1.0` effectively disables correlation removal. |
| `partition_method` | `Stratified` | CV partitioning strategy. Continuous outcomes are forced to `Random`. |
| `show_plots` | `False` | Display P1 plots interactively. Usually `False` for batch runs. |
| `one_hot_encoding` | `True` | Expand non-binary categorical features during P1 processing. |
| `cv_provided` | `False` | Import existing CV train/test files instead of creating CV splits from raw datasets. |
| `cv_input_root` | `None` | Root folder containing prebuilt `<dataset>/CVDatasets` folders when `cv_provided=True`. |
| `enable_plots` | `False` | Master toggle for optional P1 plot generation. |
| `plot_missingness` | `False` | Generate missingness plots. |
| `plot_class_counts` | `False` | Generate outcome/class count plots. |
| `plot_correlation` | `False` | Generate correlation plots. |
| `correlation_plot_max_features` | `200` | Maximum number of features included in correlation plots. |
| `plot_univariate` | `False` | Generate univariate feature analysis plots. |
| `univariate_top_k` | `20` | Number of top univariate features to display. |
| `plot_anomalies` | `False` | Generate anomaly/outlier plots when available. |
| `force` | `False` | Overwrite existing P1 outputs. Demo configs set this to `True` for easy reruns. |

### P2 Impute, Scale, And Balance

| Parameter | Default value | Description |
| --- | --- | --- |
| `scale_data` | P2 saved metadata, fallback `True` | Scale features using the selected scaler. |
| `impute_data` | P2 saved metadata, fallback `True` | Impute missing feature values. |
| `multi_impute` | P2 saved metadata, fallback `False` | Use multivariate imputation for quantitative features when supported. |
| `overwrite_cv` | `True` | Rewrite CV train/test files with P2 outputs. |
| `outcome_label` | P1 saved metadata, fallback `Class` | Outcome column. |
| `outcome_type` | P1 saved metadata, fallback `None` | Learning task type. |
| `instance_label` | P1 saved metadata, fallback `None` | Optional row identifier column. |
| `random_state` | P1/P2 saved metadata, fallback `0` | Seed for stochastic imputers or SMOTE. |
| `imputer_id` | P2 saved metadata, fallback `None` | Registry imputer ID. `None` uses the phase default. |
| `imputer_params` | P2 saved metadata, fallback `{}` | Dictionary of imputer parameters. |
| `scaler_id` | P2 saved metadata, fallback `None` | Registry scaler ID. `None` uses the phase default. |
| `scaler_params` | P2 saved metadata, fallback `{}` | Dictionary of scaler parameters. |
| `smote` | P2 saved metadata, fallback `False` | Apply classification-only oversampling to training folds after imputation and scaling. |
| `smote_method` | P2 saved metadata, fallback `auto` | `auto`, `smote`, or `smotenc`. `auto` uses SMOTENC when categorical features are present. |
| `smote_sampling_strategy` | P2 saved metadata, fallback `auto` | Sampling strategy passed to imbalanced-learn. |
| `smote_k_neighbors` | P2 saved metadata, fallback `5` | Neighbor count passed to SMOTE or SMOTENC. |
| `--list-imputers` | `False` | CLI-only utility: list discovered imputer registry IDs and exit. |
| `--list-scalers` | `False` | CLI-only utility: list discovered scaler registry IDs and exit. |

### P3 Feature Learning

| Parameter | Default value | Description |
| --- | --- | --- |
| `learner_id` | P3 saved metadata, fallback `pca` | Feature learner registry ID. |
| `learner_params` | P3 saved metadata, fallback `{}` | Dictionary of learner parameters. |
| `feature_namespace` | P3 saved metadata, fallback `FL_PCA` | Prefix/namespace for learned feature names. |
| `keep_original_features` | P3 saved metadata, fallback `True` | Keep original features alongside learned features. |
| `overwrite_cv` | `True` | Rewrite CV train/test files with P3 outputs. |
| `outcome_label` | P1 saved metadata, fallback `Class` | Outcome column. |
| `instance_label` | P1 saved metadata, fallback `None` | Optional row identifier column. |
| `random_state` | P1/P3 saved metadata, fallback `0` | Seed for stochastic learners. |
| `--list-learners` | `False` | CLI-only utility: list discovered feature-learning registry IDs and exit. |

### P4 Feature Importance

| Parameter | Default value | Description |
| --- | --- | --- |
| `models` | P4 saved metadata, fallback all registered FI methods | Feature-importance methods to run, such as `mutualinformation,multiswrfdb`. |
| `models_params` | P4 saved metadata, fallback ReBATE `n_jobs=1` defaults where applicable | Per-method parameter dictionary. STREAMLINE injects saved categorical feature indexes for ReBATE methods. |
| `top_k` | P4 saved metadata, fallback `None` | Optional top-k selector control for model-specific selected outputs. |
| `threshold` | P4 saved metadata, fallback `None` | Optional score threshold for model-specific selected outputs. |
| `keep_original_features` | P4 saved metadata, fallback `False` | Keep original features in selected-output artifacts when generated. |
| `overwrite_cv` | `True` | Overwrite P4 model-specific outputs. Shared CV files are not mutated by P4. |
| `outcome_label` | P1 saved metadata, fallback `Class` | Outcome column. |
| `outcome_type` | P1 saved metadata, fallback `None` | Learning task type passed to compatible FI methods. |
| `instance_label` | P1 saved metadata, fallback `None` | Optional row identifier column. |
| `random_state` | P1/P4 saved metadata, fallback `0` | Seed for stochastic FI methods. |
| `instance_subset` | P4 saved metadata, fallback `None` | Optional row cap for expensive FI methods. No subsampling is used when `None`. |
| `--list-models` | `False` | CLI-only utility: list discovered feature-importance methods and exit. |

### P5 Feature Selection

| Parameter | Default value | Description |
| --- | --- | --- |
| `algorithms` | `auto` | FI algorithms considered by the selector. `auto` discovers completed P4 outputs. |
| `n_splits` | `10` | Number of CV folds expected in FI outputs. Usually inherited from `[run]`. |
| `outcome_label` | P1 saved metadata, fallback `Class` | Outcome column. |
| `instance_label` | P1 saved metadata, fallback `None` | Optional row identifier column. |
| `max_features_to_keep` | `2000` | Upper bound on selected features after combining FI rankings. |
| `filter_poor_features` | `True` | Remove features with consistently poor or zero FI evidence. |
| `overwrite_cv` | `False` | Overwrite P5 selected CV outputs. |
| `selector_id` | `default` | Feature selector registry ID. |
| `selector_params` | `{}` | Dictionary of selector parameters. |
| `export_scores` | `True` | Write feature-selection score summaries. |
| `top_features` | `20` | Number of top features shown in P5 plots/summaries. |
| `show_plots` | `False` | Display P5 plots interactively. |
| `strict_discovery` | `False` | Require all expected CV FI files for an algorithm during `auto` discovery. |
| `--list-algorithms` | `False` | CLI-only utility: list available/discovered FI algorithms and exit. |

### P6 Modeling

| Parameter | Default value | Description |
| --- | --- | --- |
| `outcome_type` | `None`; resolved from `model_type`, otherwise `Binary` | Modeling task: `Binary`, `Multiclass`, or `Continuous`. The config runner fills this from `[run]` when available. |
| `model_type` | `None` | Backward-compatible alias for `outcome_type`; prefer `outcome_type` in new configs. |
| `models` | All available non-excluded models for the task | Model registry IDs. eLCS is excluded from default discovery. |
| `model_params_json` | `None` | Optional JSON or Python-literal mapping of model IDs to parameter overrides. See [Model Parameter JSON](model_params_json.md). |
| `calibrate` | `False` | Enable probability calibration for classification models. |
| `calibrate_method` | `sigmoid` | Calibration method, usually `sigmoid` or `isotonic`. |
| `calibrate_cv` | `5` | Internal CV folds used for calibration. |
| `scoring_metric` | `balanced_accuracy` | Optuna/evaluation metric. Regression configs should use a regression metric such as `explained_variance`. |
| `metric_direction` | `maximize` | Optuna optimization direction. |
| `n_trials` | `200` | Maximum Optuna trials per model/CV job. |
| `timeout` | `900` | Maximum Optuna seconds per model/CV job. |
| `training_subsample` | `0` | Optional training subset size for models with `subsampling_allowed=True`; `0` disables subsampling. |
| `uniform_fi` | `False` | Use uniform permutation FI handling when supported. |
| `save_plot` | `False` | Save model-level plots generated during modeling. |
| `skip_completed_models` | `False` | When `True`, run only failed or missing model/CV jobs. When `False`, rerun requested jobs and overwrite artifacts. |
| `bypass_one_hot_for_native_models` | `True` | Allow the native categorical model path when P1 was run with `one_hot_encoding=False`. |
| `native_categorical_models` | `CGB,ExSTraCS` | Allowed native-categorical model IDs when one-hot encoding is bypassed. |
| `--list_models` | `False` | CLI-only utility: list default model IDs for the selected task and exit. |
| `--list_models_all` | `False` | CLI-only utility: list all registered model IDs for all tasks and exit. |

P6 records Optuna trial accounting in model outputs so reports can show how
many trials actually ran within the requested budget.

By default, P6 reruns the requested model/CV jobs and overwrites existing model
artifacts. Use `skip_completed_models = True` in a config file, or
`--skip_completed_models 1` on the P6 CLI, when you want recovery behavior that
skips completed `job_model_*` markers and runs only failed or missing jobs.

### P7 Ensembles

P7 is classification-only. The config runner skips P7 automatically for
continuous outcomes.

| Parameter | Default value | Description |
| --- | --- | --- |
| `ensembles` | `hard_voting,soft_voting,stack_lr` | Ensemble registry IDs. |
| `base_models` | `None` | Base model predictions to combine. `None` lets P7 discover compatible model outputs. |
| `meta_train_source` | `train` | Source for stacking meta-training data: `train` or `test`. |
| `calibrate` | `False` | Enable calibration for ensemble probabilities when supported. |
| `calibrate_method` | `sigmoid` | Calibration method, usually `sigmoid` or `isotonic`. |
| `calibrate_cv` | `5` | Internal CV folds used for calibration. |
| `random_state` | `0` | Seed for stochastic ensemble behavior. |
| `--list_ensembles` | `False` | CLI-only utility: list ensemble registry IDs and exit. |

### P8 Summary Statistics

| Parameter | Default value | Description |
| --- | --- | --- |
| `outcome_type` | P1/P6 saved metadata, fallback `Binary` | Learning task used to choose classification or regression summaries. |
| `scoring_metric` | `balanced_accuracy` | Primary metric label used in summaries. |
| `metric_weight` | `balanced_accuracy` | Metric used to weight composite model FI plots. Continuous outcomes default to `explained_variance` if an incompatible metric is supplied. |
| `top_features` | `40` | Number of top features shown in composite FI visualizations. |
| `sig_cutoff` | `0.05` | Statistical significance threshold for comparisons. |
| `scale_data` | `True` | Metadata/reporting flag indicating whether scaled data are being summarized. |
| `exclude_plots` | `None` or empty string | Comma-separated plots to skip, such as `plot_ROC,plot_PRC,plot_FI_box,plot_metric_boxplots`. |
| `show_plots` | `False` | Display P8 plots interactively. |
| `include_ensembles` | `True` | Include P7 ensemble outputs in summaries when present. |
| `multiclass_average` | `micro` | Multiclass averaging mode for ROC/PRC summaries: `micro` or `macro`. |

### P9 Compare Datasets

| Parameter | Default value | Description |
| --- | --- | --- |
| `outcome_label` | `Class` | Outcome column. |
| `outcome_type` | `Binary` | Learning task type. |
| `instance_label` | `None` | Optional row identifier column. |
| `sig_cutoff` | `0.05` | Statistical significance threshold for between-dataset comparisons. |
| `show_plots` | `False` | Display P9 plots interactively. |

P9 compares datasets within the same experiment and writes a skipped marker when
fewer than two dataset folders with `CVDatasets/` are present.

### P10 Replication

| Parameter | Default value | Description |
| --- | --- | --- |
| `rep_data_path` | Required | Folder containing external replication datasets. |
| `dataset_for_rep` | Required | Original training dataset path used to identify the trained dataset output folder. |
| `outcome_label` | P1 saved metadata | Optional override for the outcome column. |
| `instance_label` | P1 saved metadata | Optional override for the row identifier column. |
| `match_label` | `None` | Optional label used to match or harmonize replication inputs. |
| `exclude_plots` | `None` | Comma-separated plots to skip, such as `plot_ROC`, `plot_PRC`, `plot_metric_boxplots`, `plot_FI_box`, or `feature_correlations`. |
| `show_plots` | `False` | Display replication plots interactively. |

### P11 Reporting

| Parameter | Default value | Description |
| --- | --- | --- |
| `experiment_path` | Required unless `output_path` and `experiment_name` are provided | Direct path to the experiment output folder. |
| `output_path` | Required unless `experiment_path` is provided | Parent output folder. |
| `experiment_name` | Required unless `experiment_path` is provided | Experiment folder name. |
| `reporting_dir` | `None` | Optional directory for report artifacts. `None` uses the standard experiment reporting folders. |
| `report_modes` | `standard` in config runner unless set | Config-runner convenience parameter for generating multiple report modes, such as `standard,replication`. |
| `report_mode` | `standard` | Single report mode: `standard` or `replication`. |
| `outcome_label` | `Class` in runner, often loaded from metadata/report data | Outcome column used in report labels. |
| `outcome_type` | `Binary` in runner, often loaded from metadata/report data | Learning task type used in report labels and metric filtering. |
| `instance_label` | `None` | Optional row identifier column. |
| `make_pdf` | `True` | Export a PDF report. |
| `enable_plots` | `True` | Generate missing report plots when possible. |
| `reuse_existing_figures` | `True` | Reuse existing report figure PNGs when available. Set to `False` to regenerate report figures. |

### Saved Run Command Controls

All phase CLIs support these run-command controls.

| Flag | Default value | Description |
| --- | --- | --- |
| `--ignore_saved_run_command` | `False` | Ignore `run_commands.pickle` for this run. |
| `--no_update_saved_run_command` | `False` | Do not update `run_commands.pickle` after the run. |

Use these flags when you want to run a phase with explicit command-line values
instead of reusing arguments saved from a previous run.