DOC Sort estimator tags in alphabetical order
@@ -481,37 +481,29 @@ runtime. The default values for the estimator tags are defined in the
The current set of estimator tags are:
-non_deterministic (default=``False``)
- whether the estimator is not deterministic given a fixed ``random_state``
-
-requires_positive_X (default=``False``)
- whether the estimator requires positive X.
+allow_nan (default=``False``)
+ whether the estimator supports data with missing values encoded as np.NaN
-requires_positive_y (default=``False``)
- whether the estimator requires a positive y (only applicable for regression).
+binary_only (default=``False``)
+ whether estimator supports binary classification but lacks multi-class
+ classification support.
-no_validation (default=``False``)
- whether the estimator skips input-validation. This is only meant for
- stateless and dummy transformers!
+multilabel (default=``False``)
+ whether the estimator supports multilabel output
multioutput - unused for now (default=``False``)
whether a regressor supports multi-target outputs or a classifier supports
multi-class multi-output.
-multilabel (default=``False``)
- whether the estimator supports multilabel output
-
-stateless (default=``False``)
- whether the estimator needs access to data for fitting. Even though an
- estimator is stateless, it might still need a call to ``fit`` for
- initialization.
+multioutput_only (default=``False``)
+ whether estimator supports only multi-output classification or regression.
-requires_fit (default=``True``)
- whether the estimator requires to be fitted before calling one of
- `transform`, `predict`, `predict_proba`, or `decision_function`.
+no_validation (default=``False``)
+ whether the estimator skips input-validation. This is only meant for
+ stateless and dummy transformers!
-allow_nan (default=``False``)
- whether the estimator supports data with missing values encoded as np.NaN
+non_deterministic (default=``False``)
+ whether the estimator is not deterministic given a fixed ``random_state``
poor_score (default=``False``)
whether the estimator fails to provide a "reasonable" test-set score, which
@@ -521,17 +513,25 @@ poor_score (default=``False``)
are based on current estimators in sklearn and might be replaced by
something more systematic.
-multioutput_only (default=``False``)
- whether estimator supports only multi-output classification or regression.
+requires_fit (default=``True``)
+ whether the estimator requires to be fitted before calling one of
+ `transform`, `predict`, `predict_proba`, or `decision_function`.
-binary_only (default=``False``)
- whether estimator supports binary classification but lacks multi-class
- classification support.
+requires_positive_X (default=``False``)
+ whether the estimator requires positive X.
+
+requires_positive_y (default=``False``)
+ whether the estimator requires a positive y (only applicable for regression).
_skip_test (default=``False``)
whether to skip common tests entirely. Don't use this unless you have a
*very good* reason.
+stateless (default=``False``)
+ whether the estimator needs access to data for fitting. Even though an
+ estimator is stateless, it might still need a call to ``fit`` for
+ initialization.
+
X_types (default=``['2darray']``)
Supported input types for X as list of strings. Tests are currently only
run if '2darray' is contained in the list, signifying that the estimator
