skpro.model_selection.GridSearchCV#

class skpro.model_selection.GridSearchCV(estimator, cv, param_grid, scoring=None, refit=True, verbose=0, return_n_best_estimators=1, backend='loky', error_score=nan, backend_params=None)[source]#

Perform grid-search cross-validation to find optimal model parameters.

The estimator is fit on the initial window and then temporal cross-validation is used to find the optimal parameter.

Grid-search cross-validation is performed based on a cross-validation iterator encoding the cross-validation scheme, the parameter grid to search over, and (optionally) the evaluation metric for comparing model performance. As in scikit-learn, tuning works through the common hyper-parameter interface which allows to repeatedly fit and evaluate the same estimator with different hyper-parameters.

Parameters:

estimatorestimator object

The estimator should implement the skpro estimator interface. Either the estimator must contain a “score” function, or a scoring function must be passed.

cvcross-validation generator or an iterable

e.g. KFold(n_splits=3)

param_griddict or list of dictionaries

Model tuning parameters of the estimator to evaluate

scoringskpro metric (BaseMetric), str, or callable, optional (default=None)

scoring metric to use in tuning the estimator

skpro metric objects (BaseMetric) descendants can be searched

with the registry.all_estimators search utility, for instance via all_estimators("metric", as_dataframe=True)

If callable, must have signature

(y_true: pd.DataFrame, y_pred: BaseDistribution) -> float, assuming y_true, y_pred are of the same length, lower being better, Metrics in skpro.metrics are all of this form.

If str, uses registry.resolve_alias to resolve to one of the above. Valid strings are valid registry.craft specs, which include string repr-s of any BaseMetric object, e.g., “MeanSquaredError()”; and keys of registry.ALIAS_DICT referring to metrics.
If None, defaults to CRPS()

refitbool, optional (default=True)

True = refit the estimator with the best parameters on the entire data in fit False = no refitting takes place. The estimator cannot be used to predict. This is to be used to tune the hyperparameters, and then use the estimator as a parameter estimator, e.g., via get_fitted_params or PluginParamsestimator.

verbose: int, optional (default=0)

return_n_best_estimatorsint, default=1

In case the n best estimator should be returned, this value can be set and the n best estimators will be assigned to n_best_estimators_

error_scorenumeric value or the str ‘raise’, optional (default=np.nan)

The test score returned when a estimator fails to be fitted.

return_train_scorebool, optional (default=False)

backend{“dask”, “loky”, “multiprocessing”, “threading”}, by default “loky”.

Runs parallel evaluate if specified and strategy is set as “refit”.

“None”: executes loop sequentally, simple list comprehension
“loky”, “multiprocessing” and “threading”: uses joblib.Parallel loops
“joblib”: custom and 3rd party joblib backends, e.g., spark
“dask”: uses dask, requires dask package in environment

Recommendation: Use “dask” or “loky” for parallel evaluate. “threading” is unlikely to see speed ups due to the GIL and the serialization backend (cloudpickle) for “dask” and “loky” is generally more robust than the standard pickle library used in “multiprocessing”.

error_score“raise” or numeric, default=np.nan

Value to assign to the score if an exception occurs in estimator fitting. If set to “raise”, the exception is raised. If a numeric value is given, FitFailedWarning is raised.

backend_paramsdict, optional

additional parameters passed to the backend as config. Directly passed to utils.parallel.parallelize. Valid keys depend on the value of backend:

“None”: no additional parameters, backend_params is ignored
“loky”, “multiprocessing” and “threading”: default joblib backends any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, with the exception of backend which is directly controlled by backend. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.
“joblib”: custom and 3rd party joblib backends, e.g., spark. any valid keys for joblib.Parallel can be passed here, e.g., n_jobs, backend must be passed as a key of backend_params in this case. If n_jobs is not passed, it will default to -1, other parameters will default to joblib defaults.
“dask”: any valid keys for dask.compute can be passed, e.g., scheduler

Attributes:

best_index_int
best_score_: float: Score of the best model
best_params_dict: Best parameter values across the parameter grid
best_estimator_estimator: Fitted estimator with the best parameters
cv_results_dict: Results from grid search cross validation
n_splits_: int: Number of splits in the data for cross validation
refit_time_float: Time (seconds) to refit the best estimator
scorer_function: Function used to score model
n_best_estimators_: list of tuples (“rank”, <estimator>): The “rank” is in relation to best_estimator_
n_best_scores_: list of float: The scores of n_best_estimators_ sorted from best to worst score of estimators

Methods

`check_is_fitted`([method_name])	Check if the estimator has been fitted.
`clone`()	Obtain a clone of the object with same hyper-parameters and config.
`clone_tags`(estimator[, tag_names])	Clone tags from another object as dynamic override.
`create_test_instance`([parameter_set])	Construct an instance of the class, using first test parameter set.
`create_test_instances_and_names`([parameter_set])	Create list of all test instances and a list of names for them.
`fit`(X, y[, C])	Fit regressor to training data.
`get_class_tag`(tag_name[, tag_value_default])	Get class tag value from class, with tag level inheritance from parents.
`get_class_tags`()	Get class tags from class, with tag level inheritance from parent classes.
`get_config`()	Get config flags for self.
`get_fitted_params`([deep])	Get fitted parameters.
`get_param_defaults`()	Get object's parameter defaults.
`get_param_names`([sort])	Get object's parameter names.
`get_params`([deep])	Get a dict of parameters values for this object.
`get_tag`(tag_name[, tag_value_default, ...])	Get tag value from instance, with tag level inheritance and overrides.
`get_tags`()	Get tags from instance, with tag level inheritance and overrides.
`get_test_params`([parameter_set])	Return testing parameter settings for the estimator.
`is_composite`()	Check if the object is composed of other BaseObjects.
`predict`(X)	Predict labels for data from features.
`predict_interval`([X, coverage])	Compute/return interval predictions.
`predict_proba`(X)	Predict distribution over labels for data from features.
`predict_quantiles`([X, alpha])	Compute/return quantile predictions.
`predict_var`([X])	Compute/return variance predictions.
`reset`()	Reset the object to a clean post-init state.
`set_config`(**config_dict)	Set config flags to given values.
`set_params`(**params)	Set the parameters of this object.
`set_random_state`([random_state, deep, ...])	Set random_state pseudo-random seed parameters for self.
`set_tags`(**tag_dict)	Set instance level tag overrides to given values.
`update`(X, y[, C])	Update regressor with a new batch of training data.

Examples

>>> import pandas as pd
>>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import LinearRegression
>>> from sklearn.model_selection import KFold, ShuffleSplit, train_test_split

>>> from skpro.metrics import CRPS
>>> from skpro.model_selection import GridSearchCV
>>> from skpro.regression.residual import ResidualDouble

>>> X, y = load_diabetes(return_X_y=True, as_frame=True)
>>> y = pd.DataFrame(y)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y)

>>> cv = KFold(n_splits=3)

>>> estimator = ResidualDouble(LinearRegression())
>>> param_grid = {"estimator__fit_intercept" : [True, False]}
>>> gscv = GridSearchCV(
...     estimator=estimator,
...     param_grid=param_grid,
...     cv=cv,
...     scoring=CRPS(),
... )
>>> gscv.fit(X_train, y_train)
GridSearchCV(...)
>>> y_pred = gscv.predict(X_test)
>>> y_pred_proba = gscv.predict_proba(X_test)

classmethod get_test_params(parameter_set='default')[source]#

Return testing parameter settings for the estimator.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

paramsdict or list of dict

check_is_fitted(method_name=None)[source]#

Check if the estimator has been fitted.

Check if _is_fitted attribute is present and True. The is_fitted attribute should be set to True in calls to an object’s fit method.

If not, raises a NotFittedError.

Parameters:

method_namestr, optional: Name of the method that called this function. If provided, the error message will include this information.

Raises:

NotFittedError: If the estimator has not been fitted yet.

clone()[source]#

Obtain a clone of the object with same hyper-parameters and config.

A clone is a different object without shared references, in post-init state. This function is equivalent to returning sklearn.clone of self.

Equivalent to constructing a new instance of type(self), with parameters of self, that is, type(self)(**self.get_params(deep=False)).

If configs were set on self, the clone will also have the same configs as the original, equivalent to calling cloned_self.set_config(**self.get_config()).

Also equivalent in value to a call of self.reset, with the exception that clone returns a new object, instead of mutating self like reset.

Raises:

RuntimeError if the clone is non-conforming, due to faulty __init__.

clone_tags(estimator, tag_names=None)[source]#

Clone tags from another object as dynamic override.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

clone_tags sets dynamic tag overrides from another object, estimator.

The clone_tags method should be called only in the __init__ method of an object, during construction, or directly after construction via __init__.

The dynamic tags are set to the values of the tags in estimator, with the names specified in tag_names.

The default of tag_names writes all tags from estimator to self.

Current tag values can be inspected by get_tags or get_tag.

Parameters:

estimatorAn instance of :class:BaseObject or derived class
tag_namesstr or list of str, default = None: Names of tags to clone. The default (None) clones all tags from estimator.

Returns:

self: Reference to self.

classmethod create_test_instance(parameter_set='default')[source]#

Construct an instance of the class, using first test parameter set.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

instanceinstance of the class with default parameters

classmethod create_test_instances_and_names(parameter_set='default')[source]#

Create list of all test instances and a list of names for them.

Parameters:

parameter_setstr, default=”default”: Name of the set of test parameters to return, for use in tests. If no special parameters are defined for a value, will return “default” set.

Returns:

objslist of instances of cls: i-th instance is cls(**cls.get_test_params()[i])
nameslist of str, same length as objs: i-th element is name of i-th instance of obj in tests. The naming convention is {cls.__name__}-{i} if more than one instance, otherwise {cls.__name__}

fit(X, y, C=None)[source]#

Fit regressor to training data.

Writes to self:: Sets fitted model attributes ending in “_”.

Changes state to “fitted” = sets is_fitted flag to True

Parameters:

Xpandas DataFrame: feature instances to fit regressor to
ypd.DataFrame, must be same length as X: labels to fit regressor to
Cignored, optional (default=None): censoring information for survival analysis All probabilistic regressors assume data to be uncensored, and ignore this input unless the capability:survival tag is True. Present for API consistency with survival regressors.

Returns:

selfreference to self

classmethod get_class_tag(tag_name, tag_value_default=None)[source]#

Get class tag value from class, with tag level inheritance from parents.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

The get_class_tag method is a class method, and retrieves the value of a tag taking into account only class-level tag values and overrides.

It returns the value of the tag with name tag_name from the object, taking into account tag overrides, in the following order of descending priority:

Tags set in the _tags attribute of the class.
Tags set in the _tags attribute of parent classes,

in order of inheritance.

Does not take into account dynamic tag overrides on instances, set via set_tags or clone_tags, that are defined on instances.

To retrieve tag values with potential instance overrides, use the get_tag method instead.

Parameters:

tag_namestr: Name of tag value.
tag_value_defaultany type: Default/fallback value if tag is not found.

Returns:

tag_value: Value of the tag_name tag in self. If not found, returns tag_value_default.

classmethod get_class_tags()[source]#

Get class tags from class, with tag level inheritance from parent classes.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

The get_class_tags method is a class method, and retrieves the value of a tag taking into account only class-level tag values and overrides.

It returns a dictionary with keys being keys of any attribute of _tags set in the class or any of its parent classes.

Values are the corresponding tag values, with overrides in the following order of descending priority:

Tags set in the _tags attribute of the class.
Tags set in the _tags attribute of parent classes,

in order of inheritance.

Instances can override these tags depending on hyper-parameters.

To retrieve tags with potential instance overrides, use the get_tags method instead.

Does not take into account dynamic tag overrides on instances, set via set_tags or clone_tags, that are defined on instances.

For including overrides from dynamic tags, use get_tags.

Returns:

collected_tagsdict: Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance. NOT overridden by dynamic tags set by set_tags or clone_tags.

get_config()[source]#

Get config flags for self.

Configs are key-value pairs of self, typically used as transient flags for controlling behaviour.

get_config returns dynamic configs, which override the default configs.

Default configs are set in the class attribute _config of the class or its parent classes, and are overridden by dynamic configs set via set_config.

Configs are retained under clone or reset calls.

Returns:

config_dictdict: Dictionary of config name : config value pairs. Collected from _config class attribute via nested inheritance and then any overrides and new tags from _onfig_dynamic object attribute.

get_fitted_params(deep=True)[source]#

Get fitted parameters.

State required:: Requires state to be “fitted”.

Parameters:

deepbool, default=True

Whether to return fitted parameters of components.

If True, will return a dict of parameter name : value for this object, including fitted parameters of fittable components (= BaseEstimator-valued parameters).
If False, will return a dict of parameter name : value for this object, but not include fitted parameters of components.

Returns:

fitted_paramsdict with str-valued keys

Dictionary of fitted parameters, paramname : paramvalue keys-value pairs include:

always: all fitted parameters of this object, as via get_param_names values are fitted parameter value for that key, of this object
if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value
if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc

classmethod get_param_defaults()[source]#

Get object’s parameter defaults.

Returns:

default_dict: dict[str, Any]: Keys are all parameters of cls that have a default defined in __init__. Values are the defaults, as defined in __init__.

classmethod get_param_names(sort=True)[source]#

Get object’s parameter names.

Parameters:

sortbool, default=True: Whether to return the parameter names sorted in alphabetical order (True), or in the order they appear in the class __init__ (False).

Returns:

param_names: list[str]: List of parameter names of cls. If sort=False, in same order as they appear in the class __init__. If sort=True, alphabetically ordered.

get_params(deep=True)[source]#

Get a dict of parameters values for this object.

Parameters:

deepbool, default=True

Whether to return parameters of components.

If True, will return a dict of parameter name : value for this object, including parameters of components (= BaseObject-valued parameters).
If False, will return a dict of parameter name : value for this object, but not include parameters of components.

Returns:

paramsdict with str-valued keys

Dictionary of parameters, paramname : paramvalue keys-value pairs include:

always: all parameters of this object, as via get_param_names values are parameter value for that key, of this object values are always identical to values passed at construction
if deep=True, also contains keys/value pairs of component parameters parameters of components are indexed as [componentname]__[paramname] all parameters of componentname appear as paramname with its value
if deep=True, also contains arbitrary levels of component recursion, e.g., [componentname]__[componentcomponentname]__[paramname], etc

get_tag(tag_name, tag_value_default=None, raise_error=True)[source]#

Get tag value from instance, with tag level inheritance and overrides.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

The get_tag method retrieves the value of a single tag with name tag_name from the instance, taking into account tag overrides, in the following order of descending priority:

Tags set via set_tags or clone_tags on the instance,

at construction of the instance.

Tags set in the _tags attribute of the class.
Tags set in the _tags attribute of parent classes,

in order of inheritance.

Parameters:

tag_namestr: Name of tag to be retrieved
tag_value_defaultany type, optional; default=None: Default/fallback value if tag is not found
raise_errorbool: whether a ValueError is raised when the tag is not found

Returns:

tag_valueAny: Value of the tag_name tag in self. If not found, raises an error if raise_error is True, otherwise it returns tag_value_default.

Raises:

ValueError, if raise_error is True.: The ValueError is then raised if tag_name is not in self.get_tags().keys().

get_tags()[source]#

Get tags from instance, with tag level inheritance and overrides.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

The get_tags method returns a dictionary of tags, with keys being keys of any attribute of _tags set in the class or any of its parent classes, or tags set via set_tags or clone_tags.

Values are the corresponding tag values, with overrides in the following order of descending priority:

Tags set via set_tags or clone_tags on the instance,

at construction of the instance.

Tags set in the _tags attribute of the class.
Tags set in the _tags attribute of parent classes,

in order of inheritance.

Returns:

collected_tagsdict: Dictionary of tag name : tag value pairs. Collected from _tags class attribute via nested inheritance and then any overrides and new tags from _tags_dynamic object attribute.

is_composite()[source]#

Check if the object is composed of other BaseObjects.

A composite object is an object which contains objects, as parameters. Called on an instance, since this may differ by instance.

Returns:

composite: bool: Whether an object has any parameters whose values are BaseObject descendant instances.

property is_fitted[source]#

Whether fit has been called.

Inspects object’s _is_fitted` attribute that should initialize to ``False during object construction, and be set to True in calls to an object’s fit method.

Returns:

bool: Whether the estimator has been fit.

property name[source]#: Return the name of the object or estimator.

predict(X)[source]#

Predict labels for data from features.

State required:: Requires state to be “fitted”.
Accesses in self:: Fitted model attributes ending in “_”

Parameters:

Xpandas DataFrame, must have same columns as X in fit: data to predict labels for

Returns:

ypandas DataFrame, same length as X: labels predicted for X

predict_interval(X=None, coverage=0.9)[source]#

Compute/return interval predictions.

If coverage is iterable, multiple intervals will be calculated.

State required:: Requires state to be “fitted”.
Accesses in self:: Fitted model attributes ending in “_”.

Parameters:

Xpandas DataFrame, must have same columns as X in fit: data to predict labels for
coveragefloat or list of float of unique values, optional (default=0.90): nominal coverage(s) of predictive interval(s)

Returns:

pred_intpd.DataFrame: Column has multi-index: first level is variable name from y in fit, second level coverage fractions for which intervals were computed, in the same order as in input coverage. Third level is string “lower” or “upper”, for lower/upper interval end. Row index is equal to row index of X. Entries are lower/upper bounds of interval predictions, for var in col index, at nominal coverage in second col index, lower/upper depending on third col index, for the row index. Upper/lower interval end are equivalent to quantile predictions at alpha = 0.5 - c/2, 0.5 + c/2 for c in coverage.

predict_proba(X)[source]#

Predict distribution over labels for data from features.

State required:: Requires state to be “fitted”.
Accesses in self:: Fitted model attributes ending in “_”

Parameters:

Xpandas DataFrame, must have same columns as X in fit: data to predict labels for

Returns:

yskpro BaseDistribution, same length as X: labels predicted for X

predict_quantiles(X=None, alpha=None)[source]#

Compute/return quantile predictions.

If alpha is iterable, multiple quantiles will be calculated.

State required:: Requires state to be “fitted”.
Accesses in self:: Fitted model attributes ending in “_”.

Parameters:

Xpandas DataFrame, must have same columns as X in fit: data to predict labels for
alphafloat or list of float of unique values, optional (default=[0.05, 0.95]): A probability or list of, at which quantile predictions are computed.

Returns:

quantilespd.DataFrame: Column has multi-index: first level is variable name from y in fit, second level being the values of alpha passed to the function. Row index is equal to row index of X. Entries are quantile predictions, for var in col index, at quantile probability in second col index, for the row index.

predict_var(X=None)[source]#

Compute/return variance predictions.

State required:: Requires state to be “fitted”.
Accesses in self:: Fitted model attributes ending in “_”.

Parameters:

Xpandas DataFrame, must have same columns as X in fit: data to predict labels for

Returns:

pred_varpd.DataFrame: Column names are exactly those of y passed in fit. Row index is equal to row index of X. Entries are variance prediction, for var in col index. A variance prediction for given variable and fh index is a predicted variance for that variable and index, given observed data.

reset()[source]#

Reset the object to a clean post-init state.

Results in setting self to the state it had directly after the constructor call, with the same hyper-parameters. Config values set by set_config are also retained.

A reset call deletes any object attributes, except:

hyper-parameters = arguments of __init__ written to self, e.g., self.paramname where paramname is an argument of __init__
object attributes containing double-underscores, i.e., the string “__”. For instance, an attribute named “__myattr” is retained.
config attributes, configs are retained without change. That is, results of get_config before and after reset are equal.

Class and object methods, and class attributes are also unaffected.

Equivalent to clone, with the exception that reset mutates self instead of returning a new object.

After a self.reset() call, self is equal in value and state, to the object obtained after a constructor call``type(self)(**self.get_params(deep=False))``.

Returns:

self: Instance of class reset to a clean post-init state but retaining the current hyper-parameter values.

set_config(**config_dict)[source]#

Set config flags to given values.

Configs are key-value pairs of self, typically used as transient flags for controlling behaviour.

set_config sets dynamic configs, which override the default configs.

Default configs are set in the class attribute _config of the class or its parent classes, and are overridden by dynamic configs set via set_config.

Configs are retained under clone or reset calls.

Parameters:

config_dictdict: Dictionary of config name : config value pairs.

Returns:

selfreference to self.

Notes

Changes object state, copies configs in config_dict to self._config_dynamic.

set_params(**params)[source]#

Set the parameters of this object.

The method works on simple skbase objects as well as on composite objects. Parameter key strings <component>__<parameter> can be used for composites, i.e., objects that contain other objects, to access <parameter> in the component <component>. The string <parameter>, without <component>__, can also be used if this makes the reference unambiguous, e.g., there are no two parameters of components with the name <parameter>.

Parameters:

**paramsdict: BaseObject parameters, keys must be <component>__<parameter> strings. __ suffixes can alias full strings, if unique among get_params keys.

Returns:

selfreference to self (after parameters have been set)

set_random_state(random_state=None, deep=True, self_policy='copy')[source]#

Set random_state pseudo-random seed parameters for self.

Finds random_state named parameters via self.get_params, and sets them to integers derived from random_state via set_params. These integers are sampled from chain hashing via sample_dependent_seed, and guarantee pseudo-random independence of seeded random generators.

Applies to random_state parameters in self, depending on self_policy, and remaining component objects if and only if deep=True.

Note: calls set_params even if self does not have a random_state, or none of the components have a random_state parameter. Therefore, set_random_state will reset any scikit-base object, even those without a random_state parameter.

Parameters:

random_stateint, RandomState instance or None, default=None

Pseudo-random number generator to control the generation of the random integers. Pass int for reproducible output across multiple function calls.

deepbool, default=True

Whether to set the random state in skbase object valued parameters, i.e., component estimators.

If False, will set only self’s random_state parameter, if exists.
If True, will set random_state parameters in component objects as well.

self_policystr, one of {“copy”, “keep”, “new”}, default=”copy”

“copy” : self.random_state is set to input random_state
“keep” : self.random_state is kept as is
“new” : self.random_state is set to a new random state,

derived from input random_state, and in general different from it

Returns:

selfreference to self

set_tags(**tag_dict)[source]#

Set instance level tag overrides to given values.

Every scikit-base compatible object has a dictionary of tags. Tags may be used to store metadata about the object, or to control behaviour of the object.

Tags are key-value pairs specific to an instance self, they are static flags that are not changed after construction of the object.

set_tags sets dynamic tag overrides to the values as specified in tag_dict, with keys being the tag name, and dict values being the value to set the tag to.

The set_tags method should be called only in the __init__ method of an object, during construction, or directly after construction via __init__.

Current tag values can be inspected by get_tags or get_tag.

Parameters:

**tag_dictdict: Dictionary of tag name: tag value pairs.

Returns:

Self: Reference to self.

update(X, y, C=None)[source]#

Update regressor with a new batch of training data.

Only estimators with the capability:update tag (value True) provide this method, otherwise the method ignores the call and discards the data passed.

State required:: Requires state to be “fitted”.
Writes to self:: Updates fitted model attributes ending in “_”.

Parameters:

Xpandas DataFrame: feature instances to fit regressor to
ypd.DataFrame, must be same length as X: labels to fit regressor to
Cignored, optional (default=None): censoring information for survival analysis All probabilistic regressors assume data to be uncensored

Returns:

selfreference to self

skpro.model_selection.GridSearchCV#

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