# q2-classo Parameter Reference

Every parameter of every registered `q2-classo` action, with its CLI spelling,
its type as registered with QIIME 2, its default as declared in the function
signature, and the chapter where the tutorial exercises it.

`regress` and `classify` carry most of the surface area — 36 and 34 registered
parameters respectively — and almost none of them describe the model itself.
They describe the four **model-selection procedures** c-lasso can run on the same
fitted path: PATH, CV, StabSel and LAMfixed. Each procedure has its own prefix,
its own on/off switch, and its own numerical method.

Use [Troubleshooting](04_troubleshooting.md) when a flag misbehaves, and
[Command Coverage Matrix](01_command_coverage.md) for which actions the tutorial
covers.

```{important}
These tables are maintained by hand and can drift away from the plugin. The
intended end state is to capture `qiime classo <action> --help` into
`docs/_data/help/classo-<action>.txt` at build time, render it with
`{literalinclude}`, and add a CI check that asserts the documented parameter set
equals the set registered in `q2_classo/plugin_setup.py` and
`q2_classo/_dict.py`. That generation step is not wired up yet. Until it is,
treat `--help` on your own install as the final authority, and open an issue
where this page disagrees with it.
```

```{note}
Every name, type and default below was read from `q2_classo/plugin_setup.py`,
`q2_classo/_dict.py` and the function signatures in `q2_classo/_func.py`. They
have not yet been checked against captured `--help` output from a QIIME 2 2026.7
build, because that environment does not exist yet.
```

## Flag spelling and table conventions

QIIME 2 derives every CLI flag mechanically: a parameter `foo_bar` becomes
`--p-foo-bar`, an input `--i-foo-bar`, an output `--o-foo-bar`, a `Metadata`
parameter `--m-foo-bar-file`, and a `MetadataColumn` parameter the *pair*
`--m-foo-bar-file` plus `--m-foo-bar-column`. Underscores are not collapsed,
which is why the deprecated `cv__nlam` surfaces as `--p-cv--nlam` with two
dashes.

Conventions in the **Notes** column:

- **deprecated** — still accepted, emits a `DeprecationWarning`, will be removed.
- **no `Choices()`** — the plugin does not constrain the accepted strings; see
  *String parameters have no validation* below.

The **Demonstrated in** column names the chapter that exercises the parameter,
or the closest chapter that discusses it. `not demonstrated` means the parameter
is never passed in a runnable command in this tutorial — it may still be
discussed in prose or listed in a chapter's own parameter table. It says nothing
about whether the parameter works.

## `generate-data`

Builds a synthetic log-contrast problem with a known ground-truth support. Use it
to check that your invocation works before you point it at real data.

| Name | CLI flag | Type | Default | Demonstrated in | Notes |
|---|---|---|---|---|---|
| `taxa` | `--i-taxa` | `FeatureData[Taxonomy]` | `None` | [Generate Data](../03_lowdim_classo/01_generate_data.md) | If given, feature labels are drawn from the taxonomy and a tree matrix is built. |
| `n` | `--p-n` | `Int` | `100` | [Generate Data](../03_lowdim_classo/01_generate_data.md) | Number of samples. |
| `d` | `--p-d` | `Int` | `80` | [Generate Data](../03_lowdim_classo/01_generate_data.md) | Number of features. |
| `d_nonzero` | `--p-d-nonzero` | `Int` | `5` | [Generate Data](../03_lowdim_classo/01_generate_data.md) | Size of the true support. |
| `classification` | `--p-classification` | `Bool` | `False` | [Generate Data](../03_lowdim_classo/01_generate_data.md) | When `True`, the response is binary. |
| `x` | `--o-x` | `FeatureTable[Design]` | required | [Generate Data](../03_lowdim_classo/01_generate_data.md) | |
| `c` | `--o-c` | `ConstraintMatrix` | required | [Generate Data](../03_lowdim_classo/01_generate_data.md) | Zero-sum constraint. |

```{important}
**Known bug: `generate-data` writes `randomy.tsv` into your current working
directory.** The generated response vector is not returned as an artifact. The
action writes it to a fixed relative path as a side effect. Run this action from
a directory you do not mind writing into, and expect the file to be overwritten
on every call. The response you then pass to `regress` comes from that file.
```

## `transform-features`

| Name | CLI flag | Type | Default | Demonstrated in | Notes |
|---|---|---|---|---|---|
| `features` | `--i-features` | `FeatureTable[Composition \| Frequency \| Design]` | required | [Data Preparation](../03_lowdim_classo/02_data_preparation.md) | |
| `transformation` | `--p-transformation` | `Str` | `clr` | [Data Preparation](../03_lowdim_classo/02_data_preparation.md) | **`clr` is the only accepted value.** Anything else raises `ValueError`. **No `Choices()`**. |
| `coef` | `--p-coef` | `Float` | `0.5` | [Data Preparation](../03_lowdim_classo/02_data_preparation.md) | Pseudocount substituted for non-positive entries before the log. |
| `x` | `--o-x` | `FeatureTable[Design]` | required | [Data Preparation](../03_lowdim_classo/02_data_preparation.md) | |

This is a *different* CLR implementation from
[`qiime gglasso transform-features`](02_gglasso_parameters.md): it takes a
`coef` rather than a `pseudo_count`, has no `mclr` option, has no metadata
handling, and preserves the orientation it is given, centring along rows — it
assumes samples in rows, which is what the QIIME 2 `FeatureTable` view supplies
and what `regress` expects. It does not transpose, so a feature-major input is
silently CLR-ed along the wrong axis. The two are not interchangeable.

## `add-taxa`

Replaces the feature matrix `log(X)` with `log(X)A`, where `A` encodes the
taxonomic tree — the change of basis that turns a plain log-contrast model into
`trac`.

| Name | CLI flag | Type | Notes |
|---|---|---|---|
| `features` | `--i-features` | `FeatureTable[Design \| Frequency]` | |
| `weights` | `--i-weights` | `Weights` | Optional; defaults to the all-ones vector. |
| `taxa` | `--i-taxa` | `FeatureData[Taxonomy]` | Converted to a tree internally. |
| `x` | `--o-x` | `FeatureTable[Design]` | Columns are now internal nodes plus leaves. |
| `aweights` | `--o-aweights` | `Weights` | Each node's weight divided by its number of leaves. |

The action has no parameters. The leaf-count rescaling of the weights makes
coarse taxonomic ranks comparable with fine ones, and it is not optional.

## `add-covariates`

Appends metadata columns to the feature matrix and extends the constraint matrix
and weight vector to match. A covariate added this way enters the model
*without* being subject to the zero-sum constraint.

| Name | CLI flag | Type | Default | Demonstrated in | Notes |
|---|---|---|---|---|---|
| `features` | `--i-features` | `FeatureTable[Design \| Frequency]` | `None` (optional) | [Data Preparation](../03_lowdim_classo/02_data_preparation.md) | Registered as optional, but required in practice — see the note below. |
| `c` | `--i-c` | `ConstraintMatrix` | `None` (optional) | [Data Preparation](../03_lowdim_classo/02_data_preparation.md) | Genuinely optional; falls back to an all-ones constraint row. |
| `weights` | `--i-weights` | `Weights` | `None` | [Data Preparation](../03_lowdim_classo/02_data_preparation.md) | Defaults to all ones, length = number of features. |
| `covariates` | `--m-covariates-file` | `Metadata` | **required** | [Data Preparation](../03_lowdim_classo/02_data_preparation.md) | The metadata file the columns come from. |
| `to_add` | `--p-to-add` | `List[Str]` | **required** | [Data Preparation](../03_lowdim_classo/02_data_preparation.md) | Column names to append; repeat the flag once per column. |
| `rescale` | `--p-rescale` | `List[Bool]` | `None` | [Cross-Validation](../04_highdim_atacama/05_classo_cv.md) | One value per entry of `to_add`, or a length mismatch raises `ValueError`. Defaults to all `False`. |
| `w_to_add` | `--p-w-to-add` | `List[Float]` | `None` | [Data Preparation](../03_lowdim_classo/02_data_preparation.md) | Penalty weight per added covariate; same length rule. Defaults to all `1.0`. |
| `new_features` | `--o-new-features` | `FeatureTable[Design]` | required | [Data Preparation](../03_lowdim_classo/02_data_preparation.md) | |
| `new_c` | `--o-new-c` | `ConstraintMatrix` | required | [Data Preparation](../03_lowdim_classo/02_data_preparation.md) | Zeros in the new columns, i.e. the added covariates are exempt from the constraint. |
| `new_w` | `--o-new-w` | `Weights` | required | [Data Preparation](../03_lowdim_classo/02_data_preparation.md) | |

```{note}
`--i-features` and `--i-c` are both registered as optional inputs (`--help`
prints `[optional]` for both), but only `--i-c` is genuinely optional: omitting
it falls back to an all-ones constraint row. Omitting `--i-features` fails with
`AttributeError: 'NoneType' object has no attribute 'columns'` rather than a CLI
usage error, so treat it as required. Only `--m-covariates-file` and
`--p-to-add` are marked `[required]`.
```

Two behaviours affect the design matrix this action produces:

**Categorical columns.** These are one-hot expanded, and the generated column
labels have the form `<name> = <value>` — with spaces around the equals sign.
Those labels appear in the `summarize` coefficient plots, so a categorical
covariate contributes several rows to the output, not one.

**Rescaling.** `rescale` applies to numeric columns only, where it centres the
column and divides by its norm. The log-contrast features are CLR values of
order one, so an unrescaled covariate measured in metres or in cells per gram
will dominate or vanish relative to them under a shared penalty. Setting the
corresponding `w_to_add` entry is the other lever on the same problem.

## `regress`

Constrained sparse log-contrast regression. 36 registered parameters: 35 current
plus one deprecated alias.

### Inputs and outputs

| Name | CLI flag | Type | Notes |
|---|---|---|---|
| `features` | `--i-features` | `FeatureTable[Design \| Frequency]` | Samples in rows. |
| `c` | `--i-c` | `ConstraintMatrix` | Defaults to the zero-sum constraint when omitted. |
| `weights` | `--i-weights` | `Weights` | Per-feature penalty weights. Shorter than the feature count is padded with ones; longer is truncated. |
| `result` | `--o-result` | `CLASSOProblem` | Zarr store with every model selection that was run. |

### Response and formulation

These six choose the loss and the constraint structure — the statistical model
itself. The parameters that follow affect only how lambda is chosen.

| Parameter | CLI flag | Type | Default | Demonstrated in | Notes |
|---|---|---|---|---|---|
| `y` | `--m-y-file` / `--m-y-column` | `MetadataColumn[Numeric]` | required | [Log-contrast Regression](../03_lowdim_classo/03_regression/01_logcontrast.md) | Samples missing from `y` are dropped after an inner join and reported. |
| `do_yshift` | `--p-do-yshift` | `Bool` | `False` | [Cross-Validation](../04_highdim_atacama/05_classo_cv.md) | Centres `y` before fitting. Not present on `classify`. |
| `concomitant` | `--p-concomitant` | `Bool` | `True` | [Concomitant Formulation](../03_lowdim_classo/05_advanced/01_concomitant_formulation.md) | Joint M-estimation of the noise level sigma. **Not present on `classify`.** |
| `huber` | `--p-huber` | `Bool` | `False` | [Concomitant Formulation](../03_lowdim_classo/05_advanced/01_concomitant_formulation.md) | Robust loss; combine with `concomitant` for the Huber-concomitant formulation. |
| `rho` | `--p-rho` | `Float` | `1.345` | [Concomitant Formulation](../03_lowdim_classo/05_advanced/01_concomitant_formulation.md) | Huber transition point. Only meaningful when `huber` is `True`. `classify` defaults this to `0.0`. |
| `intercept` | `--p-intercept` | `Bool` | `True` | [Cross-Validation](../04_highdim_atacama/05_classo_cv.md) | Adds an unpenalised intercept, which appears in the output as a coefficient labelled `intercept`. |

Set `concomitant` deliberately rather than inheriting its default of `True`. It
estimates sigma jointly with beta, which makes the selected lambda scale-free
with respect to the noise level — desirable, but it also changes which numerical
methods are available and therefore what `*_numerical_method` can legally be.

### PATH parameters

Computes the full regularization path. On by default.

| Parameter | CLI flag | Type | Default | Demonstrated in | Notes |
|---|---|---|---|---|---|
| `path` | `--p-path` | `Bool` | `True` | [Model Selection](../03_lowdim_classo/05_advanced/02_model_selection.md) | Registered default is `True` despite the parameter description saying `False`. |
| `path_numerical_method` | `--p-path-numerical-method` | `Str` | `not specified` | not demonstrated | `Path-Alg`, `P-PDS`, `PF-PDS` or `DR`. The default string means "let c-lasso choose". **No `Choices()`** — and because the default is itself an unrecognised literal, a typo is silently accepted. |
| `path_n_active` | `--p-path-n-active` | `Int` | `0` | [Model Selection](../03_lowdim_classo/05_advanced/02_model_selection.md) | Stop once this many variables are active. `0` means no early stop. |
| `path_nlam_log` | `--p-path-nlam-log` | `Int` | `40` | [Model Selection](../03_lowdim_classo/05_advanced/02_model_selection.md) | Number of lambdas on the log-spaced path. |
| `path_lamin_log` | `--p-path-lamin-log` | `Float` | `1e-2` | [Model Selection](../03_lowdim_classo/05_advanced/02_model_selection.md) | Smallest lambda as a fraction of lambda_max. |

### CV parameters

K-fold cross-validation over the path. On by default.

| Parameter | CLI flag | Type | Default | Demonstrated in | Notes |
|---|---|---|---|---|---|
| `cv` | `--p-cv` | `Bool` | `True` | [Cross-Validation](../04_highdim_atacama/05_classo_cv.md) | |
| `cv_numerical_method` | `--p-cv-numerical-method` | `Str` | `not specified` | not demonstrated | As `path_numerical_method`. **No `Choices()`**. |
| `cv_seed` | `--p-cv-seed` | `Int` | `1` | [Cross-Validation](../04_highdim_atacama/05_classo_cv.md) | Fold assignment seed. Fixed by default, so repeated runs agree — change it to check fold stability. |
| `cv_one_se` | `--p-cv-one-se` | `Bool` | `True` | [Cross-Validation](../04_highdim_atacama/05_classo_cv.md) | Select lambda by the one-standard-error rule rather than the CV minimum. |
| `cv_subsets` | `--p-cv-subsets` | `Int` | `5` | [Cross-Validation](../04_highdim_atacama/05_classo_cv.md) | Number of folds. |
| `cv_nlam` | `--p-cv-nlam` | `Int` | `100` | [Model Selection](../03_lowdim_classo/05_advanced/02_model_selection.md) | Lambdas on the CV path. **Current spelling.** |
| `cv__nlam` | `--p-cv--nlam` | `Int` | `None` | [Cross-Validation](../04_highdim_atacama/05_classo_cv.md) | **Deprecated** alias of `cv_nlam` — note the double underscore and the double dash. Still works, emits a `DeprecationWarning`. Passing both raises only when `cv_nlam` was changed from its default of 100 and the two values differ; `--p-cv-nlam 100 --p-cv--nlam 50` does not raise — it silently uses 50. |
| `cv_lamin` | `--p-cv-lamin` | `Float` | `1e-3` | [Cross-Validation](../04_highdim_atacama/05_classo_cv.md) | Smallest lambda on the CV path. |
| `cv_logscale` | `--p-cv-logscale` | `Bool` | `True` | [Cross-Validation](../04_highdim_atacama/05_classo_cv.md) | Log-spaced CV path. |

```{note}
`--p-cv-nlam` and `--p-cv-lamin` define a separate grid from `--p-path-nlam-log`
and `--p-path-lamin-log`. Changing the PATH grid does not change what CV
searches over, so the CV-selected lambda can be absent from the plotted path.
```

```{important}
**Open docs issue.** The only runnable commands that pass this grid's size use
the *deprecated* spelling: [Cross-Validation](../04_highdim_atacama/05_classo_cv.md)
invokes `--p-cv--nlam`, not `--p-cv-nlam`. Rule 5 of
[Command Coverage Matrix](01_command_coverage.md) says `--p-cv--nlam` must not
appear in a runnable command outside the quarantined chapters. Migrate those two
commands to `--p-cv-nlam`.
```

### StabSel parameters

Stability selection: refit on many subsamples and keep the features selected
often enough. On by default, and generally the most trustworthy of the four
procedures for microbiome data.

| Parameter | CLI flag | Type | Default | Demonstrated in | Notes |
|---|---|---|---|---|---|
| `stabsel` | `--p-stabsel` | `Bool` | `True` | [Model Selection](../03_lowdim_classo/05_advanced/02_model_selection.md) | |
| `stabsel_numerical_method` | `--p-stabsel-numerical-method` | `Str` | `not specified` | not demonstrated | As above. **No `Choices()`**. |
| `stabsel_seed` | `--p-stabsel-seed` | `Int` | `None` | [Model Selection](../03_lowdim_classo/05_advanced/02_model_selection.md) | Unlike `cv_seed`, this defaults to unset, so subsampling is not reproducible unless you set it. Set it for anything you intend to report. |
| `stabsel_method` | `--p-stabsel-method` | `Str` | `first` | [Model Selection](../03_lowdim_classo/05_advanced/02_model_selection.md) | `first`, `lam` or `max`. Decides what is recorded per subsample. **No `Choices()`**. |
| `stabsel_lam` | `--p-stabsel-lam` | `Float` | `-1.0` | not demonstrated | Only used when `stabsel_method` is `lam`. A negative value means "use the theoretical lambda". |
| `stabsel_true_lam` | `--p-stabsel-true-lam` | `Bool` | `True` | not demonstrated | Only used when `stabsel_method` is `lam`. `True` = the value given is a real lambda; `False` = it is lambda/lambda_max in [0, 1]. |
| `stabsel_b` | `--p-stabsel-b` | `Int` | `50` | [Model Selection](../03_lowdim_classo/05_advanced/02_model_selection.md) | Number of subsamples. The main runtime knob of the whole action. |
| `stabsel_q` | `--p-stabsel-q` | `Int` | `10` | [Model Selection](../03_lowdim_classo/05_advanced/02_model_selection.md) | Variables selected per subsample. |
| `stabsel_percent_ns` | `--p-stabsel-percent-ns` | `Float` | `0.5` | [Model Selection](../03_lowdim_classo/05_advanced/02_model_selection.md) | Subsample size as a fraction of n. |
| `stabsel_lamin` | `--p-stabsel-lamin` | `Float` | `1e-2` | not demonstrated | Only used when `stabsel_method` is `max`. |
| `stabsel_threshold` | `--p-stabsel-threshold` | `Float` | `0.7` | [Model Selection](../03_lowdim_classo/05_advanced/02_model_selection.md) | Selection frequency above which a feature is reported as selected. |
| `stabsel_threshold_label` | `--p-stabsel-threshold-label` | `Float` | `0.4` | [Model Selection](../03_lowdim_classo/05_advanced/02_model_selection.md) | Frequency above which a feature is *labelled* in c-lasso's own matplotlib plot. Recorded in the artifact; not used by the QIIME 2 visualization. |

`stabsel_b`, `stabsel_q` and `stabsel_threshold` interact. Raising `q` makes each
subsample select more variables, which raises every feature's selection
frequency, so a `threshold` that was strict at `q=10` becomes permissive at
`q=30`. Change one at a time, and report all three alongside any selected feature
set.

### LAMfixed parameters

A single fit at one fixed lambda. On by default.

| Parameter | CLI flag | Type | Default | Demonstrated in | Notes |
|---|---|---|---|---|---|
| `lamfixed` | `--p-lamfixed` | `Bool` | `True` | [Model Selection](../03_lowdim_classo/05_advanced/02_model_selection.md) | |
| `lamfixed_numerical_method` | `--p-lamfixed-numerical-method` | `Str` | `not specified` | not demonstrated | As above. **No `Choices()`**. |
| `lamfixed_lam` | `--p-lamfixed-lam` | `Float` | `-1.0` | [Model Selection](../03_lowdim_classo/05_advanced/02_model_selection.md) | Negative means "use the theoretical lambda once it is computed". |
| `lamfixed_true_lam` | `--p-lamfixed-true-lam` | `Bool` | `True` | [Model Selection](../03_lowdim_classo/05_advanced/02_model_selection.md) | `True` = the value is a real lambda; `False` = it is lambda/lambda_max in [0, 1]. With `True` and `lam = -1`, the value becomes `n * theoretical_lam`. |

```{tip}
All four procedures default to on, so a bare `qiime classo regress` runs PATH,
CV, StabSel and LAMfixed in one call. StabSel refits the model `stabsel_b` times
(50 by default) and usually dominates the cost. Turn off what you are not going
to read: `--p-cv False`, `--p-stabsel False`, `--p-lamfixed False`.
```

```{note}
**`--help` misreports several of these defaults.** QIIME 2 renders the
registered description text verbatim, so `--help` contradicts its own
`[default: ...]` marker wherever the description in `q2_classo/_dict.py` has
drifted from the function signature. In every case below the marker is right and
the description is wrong. The tables above follow the signatures.

| Parameter | `--help` prose says | Signature default |
|---|---|---|
| `path` | `Default Value = False` | `True` |
| `cv` | `Default Value = False` | `True` |
| `lamfixed` | `Default Value = False` | `True` |
| `cv_seed` | `Default value : None` | `1` |
| `path_numerical_method` | `Default value : 'choose'` | `not specified` |
| `cv_numerical_method` | `Default value : 'choose'` | `not specified` |
| `stabsel_numerical_method` | `Default value : 'choose'` | `not specified` |
| `lamfixed_numerical_method` | `Default value : 'choose'` | `not specified` |
| `path_n_active` | `Dafault value : False` | `0` |
```

## `classify`

Constrained sparse classification. 34 registered parameters: 33 current plus the
same deprecated `cv__nlam` alias.

The PATH, CV, StabSel and LAMfixed blocks are identical to `regress`: same names,
same types, same defaults. Read the four tables above and substitute `classify`
for `regress`.

### Inputs and outputs

Identical to `regress`: `--i-features`, `--i-c`, `--i-weights`, `--o-result`.

### Differences from `regress`

| Parameter | CLI flag | Type | Default | Demonstrated in | Notes |
|---|---|---|---|---|---|
| `y` | `--m-y-file` / `--m-y-column` | `MetadataColumn[Categorical]` | required | [Log-contrast Classification](../03_lowdim_classo/04_classification/01_logcontrast.md) | **Categorical**, not numeric. Must be binary; a non-binary column is rejected. |
| `huber` | `--p-huber` | `Bool` | `False` | [Log-contrast Classification](../03_lowdim_classo/04_classification/01_logcontrast.md) | Huber hinge loss. |
| `rho` | `--p-rho` | `Float` | `0.0` | [Concomitant Formulation](../03_lowdim_classo/05_advanced/01_concomitant_formulation.md) | **Different default from `regress`** (`1.345`). |
| `intercept` | `--p-intercept` | `Bool` | `True` | [Cross-Validation](../04_highdim_atacama/05_classo_cv.md) | |
| `do_yshift` | — | — | — | — | **Does not exist on `classify`.** Centring a categorical response is meaningless. |
| `concomitant` | — | — | — | — | **Does not exist on `classify`.** See the explanation below. |

```{important}
**`qiime classo classify --p-concomitant` does not exist.** The concomitant
formulation is unavailable for classification: the parameter is not registered,
and the solver forces `formulation.concomitant = False` for classification
problems regardless. Passing the flag is a CLI error, not a silently ignored
option.

If you want the robustness that motivated `concomitant`, use the Huber hinge
loss instead — `--p-huber True`, tuned via `--p-rho`. `rho` defaults to `0.0`
here rather than `1.345` — it is wired to c-lasso's
`formulation.rho_classification`, a different field from `regress`'s
`formulation.rho`, and `0.0` is a legal value for it (c-lasso requires only that
it be strictly less than 1). The `--help` text for `classify` reports `1.345`
(`_dict.py:286`) and is wrong. The registered default is `0.0`, which is what
every worked `classify` command in this book runs with. See
[Concomitant Formulation](../03_lowdim_classo/05_advanced/01_concomitant_formulation.md).
```

## `predict`

| Name | CLI flag | Type | Notes |
|---|---|---|---|
| `features` | `--i-features` | `FeatureTable[Design \| Frequency]` | Columns are matched to the fitted model by label; an `intercept` column is synthesised. |
| `problem` | `--i-problem` | `CLASSOProblem` | Output of `regress` or `classify`. |
| `predictions` | `--o-predictions` | `CLASSOProblem` | One prediction set per model selection that was computed. |

The action has no parameters. It emits a prediction for every model selection
present in `problem`, so if you turned off CV and StabSel at fit time you get
correspondingly fewer prediction sets here.

## `summarize` (visualizer)

| Name | CLI flag | Type | Default | Demonstrated in | Notes |
|---|---|---|---|---|---|
| `problem` | `--i-problem` | `CLASSOProblem` | required | [Predict and Summarize](../03_lowdim_classo/06_predict_and_summarize.md) | |
| `taxa` | `--i-taxa` | `FeatureData[Taxonomy]` | `None` | [Predict and Summarize](../03_lowdim_classo/06_predict_and_summarize.md) | Used to label coefficients taxonomically. |
| `predictions` | `--i-predictions` | `CLASSOProblem` | `None` | [Predict and Summarize](../03_lowdim_classo/06_predict_and_summarize.md) | Output of `predict`. Omit it and the prediction panes are absent. |
| `maxplot` | `--p-maxplot` | `Int` | `200` | [Predict and Summarize](../03_lowdim_classo/06_predict_and_summarize.md) | Maximum number of coefficients drawn in a StabSel profile or beta bar plot. Raise it on wide problems or the plot silently truncates. |

## String parameters have no validation

`q2_classo/plugin_setup.py` imports `Choices` but never applies it. QIIME 2
therefore accepts any string for the parameters below, and the check — if there
is one — happens inside the function:

| Flag | Accepted values | Behaviour on a typo |
|---|---|---|
| `--p-transformation` | `clr` only | `ValueError: Unknown transformation name, use clr and not '...'` |
| `--p-path-numerical-method` | `Path-Alg`, `P-PDS`, `PF-PDS`, `DR` | **Silently accepted** — the default is itself the unrecognised literal `not specified`, which means "choose automatically" |
| `--p-cv-numerical-method` | same set | Silently accepted |
| `--p-stabsel-numerical-method` | same set | Silently accepted |
| `--p-lamfixed-numerical-method` | same set | Silently accepted |
| `--p-stabsel-method` | `first`, `lam`, `max` | Reaches c-lasso; behaviour depends on the solver |

The `*_numerical_method` family is the dangerous one. Because the sentinel
default is not a valid method name, there is no way for the code to distinguish
"user asked for automatic selection" from "user misspelled `Path-Alg`" — both are
strings outside the recognised set, and both fall through to automatic
selection. You will get an answer, and it will not be the method you asked for.

## See also

- [q2-gglasso Parameter Reference](02_gglasso_parameters.md)
- [Troubleshooting & Known Failure Modes](04_troubleshooting.md)
- [Command Coverage Matrix](01_command_coverage.md)