| add_indicator | If `True`, a :class:`MissingIndicator` transform will stack onto output
of the imputer's transform. This allows a predictive estimator
to account for missingness despite imputation. If a feature has no
missing values at fit/train time, the feature won't appear on
the missing indicator even if there are missing values at
transform/test time | default: false |
| estimator | The estimator to use at each step of the round-robin imputation
If `sample_posterior=True`, the estimator must support
`return_std` in its `predict` method | default: null |
| imputation_order | | default: "ascending" |
| initial_strategy | | default: "mean" |
| keep_empty_features | If True, features that consist exclusively of missing values when
`fit` is called are returned in results when `transform` is called
The imputed value is always `0` except when
`initial_strategy="constant"` in which case `fill_value` will be
used instead
.. versionadded:: 1.2 | default: false |
| max_iter | Maximum number of imputation rounds to perform before returning the
imputations computed during the final round. A round is a single
imputation of each feature with missing values. The stopping criterion
is met once `max(abs(X_t - X_{t-1}))/max(abs(X[known_vals])) < tol`,
where `X_t` is `X` at iteration `t`. Note that early stopping is only
applied if `sample_posterior=False` | default: 10 |
| max_value | Maximum possible imputed value. Broadcast to shape `(n_features,)` if
scalar. If array-like, expects shape `(n_features,)`, one max value for
each feature. The default is `np.inf`
.. versionchanged:: 0.23
Added support for array-like | default: Infinity |
| min_value | Minimum possible imputed value. Broadcast to shape `(n_features,)` if
scalar. If array-like, expects shape `(n_features,)`, one min value for
each feature. The default is `-np.inf`
.. versionchanged:: 0.23
Added support for array-like | default: -Infinity |
| missing_values | The placeholder for the missing values. All occurrences of
`missing_values` will be imputed. For pandas' dataframes with
nullable integer dtypes with missing values, `missing_values`
should be set to `np.nan`, since `pd.NA` will be converted to `np.nan` | default: NaN |
| n_nearest_features | Number of other features to use to estimate the missing values of
each feature column. Nearness between features is measured using
the absolute correlation coefficient between each feature pair (after
initial imputation). To ensure coverage of features throughout the
imputation process, the neighbor features are not necessarily nearest,
but are drawn with probability proportional to correlation for each
imputed target feature. Can provide significant speed-up when the
number of features is huge. If `None`, all features will be used
initial_strategy : {'mean', 'median', 'most_frequent', 'constant'}, default='mean'
Which strategy to use to initialize the missing values. Same as the
`strategy` parameter in :class:`~sklearn.impute.SimpleImputer`
imputation_order : {'ascending', 'descending', 'roman', 'arabic', 'random'}, default='ascending'
The order in which the features will be imputed. Possible values:
- `'ascending'`: From features with... | default: null |
| random_state | The seed of the pseudo random number generator to use. Randomizes
selection of estimator features if `n_nearest_features` is not `None`,
the `imputation_order` if `random`, and the sampling from posterior if
`sample_posterior=True`. Use an integer for determinism
See :term:`the Glossary ` | default: null |
| sample_posterior | Whether to sample from the (Gaussian) predictive posterior of the
fitted estimator for each imputation. Estimator must support
`return_std` in its `predict` method if set to `True`. Set to
`True` if using `IterativeImputer` for multiple imputations | default: false |
| skip_complete | If `True` then features with missing values during :meth:`transform`
which did not have any missing values during :meth:`fit` will be
imputed with the initial imputation method only. Set to `True` if you
have many features with no missing values at both :meth:`fit` and
:meth:`transform` time to save compute | default: false |
| tol | Tolerance of the stopping condition | default: 0.001 |
| verbose | Verbosity flag, controls the debug messages that are issued
as functions are evaluated. The higher, the more verbose. Can be 0, 1,
or 2 | default: 0 |