18701
18886
sklearn.model_selection._search.RandomizedSearchCV(estimator=sklearn.linear_model._logistic.LogisticRegression)
sklearn.RandomizedSearchCV(LogisticRegression)
sklearn.model_selection._search.RandomizedSearchCV
1
openml==0.10.2,sklearn==0.23.2
Randomized search on hyper parameters.
RandomizedSearchCV implements a "fit" and a "score" method.
It also implements "predict", "predict_proba", "decision_function",
"transform" and "inverse_transform" if they are implemented in the
estimator used.
The parameters of the estimator used to apply these methods are optimized
by cross-validated search over parameter settings.
In contrast to GridSearchCV, not all parameter values are tried out, but
rather a fixed number of parameter settings is sampled from the specified
distributions. The number of parameter settings that are tried is
given by n_iter.
If all parameters are presented as a list,
sampling without replacement is performed. If at least one parameter
is given as a distribution, sampling with replacement is used.
It is highly recommended to use continuous distributions for continuous
parameters.
2020-10-21T01:04:20
English
sklearn==0.23.2
numpy>=1.6.1
scipy>=0.9
cv
int
null
Determines the cross-validation splitting strategy
Possible inputs for cv are:
- None, to use the default 5-fold cross validation,
- integer, to specify the number of folds in a `(Stratified)KFold`,
- :term:`CV splitter`,
- An iterable yielding (train, test) splits as arrays of indices
For integer/None inputs, if the estimator is a classifier and ``y`` is
either binary or multiclass, :class:`StratifiedKFold` is used. In all
other cases, :class:`KFold` is used
Refer :ref:`User Guide <cross_validation>` for the various
cross-validation strategies that can be used here
.. versionchanged:: 0.22
``cv`` default value if None changed from 3-fold to 5-fold
error_score
'raise' or numeric
NaN
Value to assign to the score if an error occurs in estimator fitting
If set to 'raise', the error is raised. If a numeric value is given,
FitFailedWarning is raised. This parameter does not affect the refit
step, which will always raise the error
estimator
estimator object
{"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": null}}
A object of that type is instantiated for each grid point
This is assumed to implement the scikit-learn estimator interface
Either estimator needs to provide a ``score`` function,
or ``scoring`` must be passed
iid
bool
"deprecated"
If True, return the average score across folds, weighted by the number
of samples in each test set. In this case, the data is assumed to be
identically distributed across the folds, and the loss minimized is
the total loss per sample, and not the mean loss across the folds
.. deprecated:: 0.22
Parameter ``iid`` is deprecated in 0.22 and will be removed in 0.24
n_iter
int
10
Number of parameter settings that are sampled. n_iter trades
off runtime vs quality of the solution
n_jobs
int
-1
Number of jobs to run in parallel
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details
.. versionchanged:: v0.20
`n_jobs` default changed from 1 to None
param_distributions
dict or list of dicts
{"C": {"oml-python:serialized_object": "rv_frozen", "value": {"dist": "scipy.stats._continuous_distns.uniform_gen", "a": 0.0, "b": 1.0, "args": [1, 100], "kwds": {}}}, "penalty": ["l1", "l2", "elasticnet"]}
Dictionary with parameters names (`str`) as keys and distributions
or lists of parameters to try. Distributions must provide a ``rvs``
method for sampling (such as those from scipy.stats.distributions)
If a list is given, it is sampled uniformly
If a list of dicts is given, first a dict is sampled uniformly, and
then a parameter is sampled using that dict as above
pre_dispatch
int
"2*n_jobs"
Controls the number of jobs that get dispatched during parallel
execution. Reducing this number can be useful to avoid an
explosion of memory consumption when more jobs get dispatched
than CPUs can process. This parameter can be:
- None, in which case all the jobs are immediately
created and spawned. Use this for lightweight and
fast-running jobs, to avoid delays due to on-demand
spawning of the jobs
- An int, giving the exact number of total jobs that are
spawned
- A str, giving an expression as a function of n_jobs,
as in '2*n_jobs'
random_state
int or RandomState instance
0
Pseudo random number generator state used for random uniform sampling
from lists of possible values instead of scipy.stats distributions
Pass an int for reproducible output across multiple
function calls
See :term:`Glossary <random_state>`
refit
bool
true
Refit an estimator using the best found parameters on the whole
dataset
For multiple metric evaluation, this needs to be a `str` denoting the
scorer that would be used to find the best parameters for refitting
the estimator at the end
Where there are considerations other than maximum score in
choosing a best estimator, ``refit`` can be set to a function which
returns the selected ``best_index_`` given the ``cv_results``. In that
case, the ``best_estimator_`` and ``best_params_`` will be set
according to the returned ``best_index_`` while the ``best_score_``
attribute will not be available
The refitted estimator is made available at the ``best_estimator_``
attribute and permits using ``predict`` directly on this
``RandomizedSearchCV`` instance
Also for multiple metric evaluation, the attributes ``best_index_``,
``best_score_`` and ``best_params_`` will only be available if
``refit`` is set and all of them will be determined w.r.t this speci...
return_train_score
bool
false
If ``False``, the ``cv_results_`` attribute will not include training
scores
Computing training scores is used to get insights on how different
parameter settings impact the overfitting/underfitting trade-off
However computing the scores on the training set can be computationally
expensive and is not strictly required to select the parameters that
yield the best generalization performance
.. versionadded:: 0.19
.. versionchanged:: 0.21
Default value was changed from ``True`` to ``False``
scoring
str
null
A single str (see :ref:`scoring_parameter`) or a callable
(see :ref:`scoring`) to evaluate the predictions on the test set
For evaluating multiple metrics, either give a list of (unique) strings
or a dict with names as keys and callables as values
NOTE that when using custom scorers, each scorer should return a single
value. Metric functions returning a list/array of values can be wrapped
into multiple scorers that return one value each
See :ref:`multimetric_grid_search` for an example
If None, the estimator's score method is used
verbose
integer
0
Controls the verbosity: the higher, the more messages
estimator
18686
11601
sklearn.linear_model._logistic.LogisticRegression
sklearn.LogisticRegression
sklearn.linear_model._logistic.LogisticRegression
4
openml==0.10.2,sklearn==0.23.2
Logistic Regression (aka logit, MaxEnt) classifier.
In the multiclass case, the training algorithm uses the one-vs-rest (OvR)
scheme if the 'multi_class' option is set to 'ovr', and uses the
cross-entropy loss if the 'multi_class' option is set to 'multinomial'.
(Currently the 'multinomial' option is supported only by the 'lbfgs',
'sag', 'saga' and 'newton-cg' solvers.)
This class implements regularized logistic regression using the
'liblinear' library, 'newton-cg', 'sag', 'saga' and 'lbfgs' solvers. **Note
that regularization is applied by default**. It can handle both dense
and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit
floats for optimal performance; any other input format will be converted
(and copied).
The 'newton-cg', 'sag', and 'lbfgs' solvers support only L2 regularization
with primal formulation, or no regularization. The 'liblinear' solver
supports both L1 and L2 regularization, with a dual formulation only for
the L2 penalty. The Elastic-Net regularization is only su...
2020-09-18T03:11:00
English
sklearn==0.23.2
numpy>=1.6.1
scipy>=0.9
C
float
1.0
Inverse of regularization strength; must be a positive float
Like in support vector machines, smaller values specify stronger
regularization
class_weight
dict or
null
Weights associated with classes in the form ``{class_label: weight}``
If not given, all classes are supposed to have weight one
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified
.. versionadded:: 0.17
*class_weight='balanced'*
dual
bool
false
Dual or primal formulation. Dual formulation is only implemented for
l2 penalty with liblinear solver. Prefer dual=False when
n_samples > n_features
fit_intercept
bool
true
Specifies if a constant (a.k.a. bias or intercept) should be
added to the decision function
intercept_scaling
float
1
Useful only when the solver 'liblinear' is used
and self.fit_intercept is set to True. In this case, x becomes
[x, self.intercept_scaling],
i.e. a "synthetic" feature with constant value equal to
intercept_scaling is appended to the instance vector
The intercept becomes ``intercept_scaling * synthetic_feature_weight``
Note! the synthetic feature weight is subject to l1/l2 regularization
as all other features
To lessen the effect of regularization on synthetic feature weight
(and therefore on the intercept) intercept_scaling has to be increased
l1_ratio
float
null
The Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``. Only
used if ``penalty='elasticnet'``. Setting ``l1_ratio=0`` is equivalent
to using ``penalty='l2'``, while setting ``l1_ratio=1`` is equivalent
to using ``penalty='l1'``. For ``0 < l1_ratio <1``, the penalty is a
combination of L1 and L2.
max_iter
int
100
Maximum number of iterations taken for the solvers to converge
multi_class : {'auto', 'ovr', 'multinomial'}, default='auto'
If the option chosen is 'ovr', then a binary problem is fit for each
label. For 'multinomial' the loss minimised is the multinomial loss fit
across the entire probability distribution, *even when the data is
binary*. 'multinomial' is unavailable when solver='liblinear'
'auto' selects 'ovr' if the data is binary, or if solver='liblinear',
and otherwise selects 'multinomial'
.. versionadded:: 0.18
Stochastic Average Gradient descent solver for 'multinomial' case
.. versionchanged:: 0.22
Default changed from 'ovr' to 'auto' in 0.22
multi_class
"auto"
n_jobs
int
null
Number of CPU cores used when parallelizing over classes if
multi_class='ovr'". This parameter is ignored when the ``solver`` is
set to 'liblinear' regardless of whether 'multi_class' is specified or
not. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
context. ``-1`` means using all processors
See :term:`Glossary <n_jobs>` for more details
penalty
"l2"
random_state
int
null
Used when ``solver`` == 'sag', 'saga' or 'liblinear' to shuffle the
data. See :term:`Glossary <random_state>` for details
solver : {'newton-cg', 'lbfgs', 'liblinear', 'sag', 'saga'}, default='lbfgs'
Algorithm to use in the optimization problem
- For small datasets, 'liblinear' is a good choice, whereas 'sag' and
'saga' are faster for large ones
- For multiclass problems, only 'newton-cg', 'sag', 'saga' and 'lbfgs'
handle multinomial loss; 'liblinear' is limited to one-versus-rest
schemes
- 'newton-cg', 'lbfgs', 'sag' and 'saga' handle L2 or no penalty
- 'liblinear' and 'saga' also handle L1 penalty
- 'saga' also supports 'elasticnet' penalty
- 'liblinear' does not support setting ``penalty='none'``
Note that 'sag' and 'saga' fast convergence is only guaranteed on
features with approximately the same scale. You can
preprocess the data with a scaler from sklearn.preprocessing
.. versionadded:: 0.17
Stochastic Average Gr...
solver
"lbfgs"
tol
float
0.0001
Tolerance for stopping criteria
verbose
int
0
For the liblinear and lbfgs solvers set verbose to any positive
number for verbosity
warm_start
bool
false
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution
Useless for liblinear solver. See :term:`the Glossary <warm_start>`
.. versionadded:: 0.17
*warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers
openml-python
python
scikit-learn
sklearn
sklearn_0.23.2
openml-python
python
scikit-learn
sklearn
sklearn_0.23.2