432
1159
TEST719f06ea92sklearn.ensemble._voting.VotingClassifier(dt=sklearn.tree._classes.DecisionTreeClassifier)
sklearn.VotingClassifier
sklearn.ensemble._voting.VotingClassifier
1
openml==0.14.1,sklearn==0.22.2
Soft Voting/Majority Rule classifier for unfitted estimators.
.. versionadded:: 0.17
2024-01-10T15:42:39
English
sklearn==0.22.2
numpy>=1.11.0
scipy>=0.17.0
joblib>=0.11
estimators
list of
[{"oml-python:serialized_object": "component_reference", "value": {"key": "dt", "step_name": "dt"}}]
Invoking the ``fit`` method on the ``VotingClassifier`` will fit clones
of those original estimators that will be stored in the class attribute
``self.estimators_``. An estimator can be set to ``'drop'``
using ``set_params``
.. deprecated:: 0.22
Using ``None`` to drop an estimator is deprecated in 0.22 and
support will be dropped in 0.24. Use the string ``'drop'`` instead
flatten_transform
bool
true
Affects shape of transform output only when voting='soft'
If voting='soft' and flatten_transform=True, transform method returns
matrix with shape (n_samples, n_classifiers * n_classes). If
flatten_transform=False, it returns
(n_classifiers, n_samples, n_classes).
n_jobs
int or None
null
The number of jobs to run in parallel for ``fit``
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details
voting
str
"hard"
If 'hard', uses predicted class labels for majority rule voting
Else if 'soft', predicts the class label based on the argmax of
the sums of the predicted probabilities, which is recommended for
an ensemble of well-calibrated classifiers
weights
array
null
Sequence of weights (`float` or `int`) to weight the occurrences of
predicted class labels (`hard` voting) or class probabilities
before averaging (`soft` voting). Uses uniform weights if `None`
dt
423
1159
TEST719f06ea92sklearn.tree._classes.DecisionTreeClassifier
sklearn.DecisionTreeClassifier
sklearn.tree._classes.DecisionTreeClassifier
1
openml==0.14.1,sklearn==0.22.2
A decision tree classifier.
2024-01-10T15:42:36
English
sklearn==0.22.2
numpy>=1.11.0
scipy>=0.17.0
joblib>=0.11
ccp_alpha
non
0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details
.. versionadded:: 0.22
class_weight
dict
null
Weights associated with classes in the form ``{class_label: weight}``
If None, all classes are supposed to have weight one. For
multi-output problems, a list of dicts can be provided in the same
order as the columns of y
Note that for multioutput (including multilabel) weights should be
defined for each class of every column in its own dict. For example,
for four-class multilabel classification weights should be
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
[{1:1}, {2:5}, {3:1}, {4:1}]
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))``
For multi-output, the weights of each column of y will be multiplied
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified
criterion
"gini"
max_depth
int
2
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples
max_features
int
null
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split
- If float, then `max_features` is a fraction and
`int(max_features * n_features)` features are considered at each
split
- If "auto", then `max_features=sqrt(n_features)`
- If "sqrt", then `max_features=sqrt(n_features)`
- If "log2", then `max_features=log2(n_features)`
- If None, then `max_features=n_features`
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features
max_leaf_nodes
int
null
Grow a tree with ``max_leaf_nodes`` in best-first fashion
Best nodes are defined as relative reduction in impurity
If None then unlimited number of leaf nodes
min_impurity_decrease
float
0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed
.. versionadded:: 0.19
min_impurity_split
float
null
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf
.. deprecated:: 0.19
``min_impurity_split`` has been deprecated in favor of
``min_impurity_decrease`` in 0.19. The default value of
``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it
will be removed in 0.25. Use ``min_impurity_decrease`` instead
min_samples_leaf
int or float
1
The minimum number of samples required to be at a leaf node
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression
- If int, then consider `min_samples_leaf` as the minimum number
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node
.. versionchanged:: 0.18
Added float values for fractions
min_samples_split
int or float
2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split
.. versionchanged:: 0.18
Added float values for fractions
min_weight_fraction_leaf
float
0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided
presort
deprecated
"deprecated"
This parameter is deprecated and will be removed in v0.24
.. deprecated:: 0.22
random_state
int or RandomState
null
If int, random_state is the seed used by the random number generator;
If RandomState instance, random_state is the random number generator;
If None, the random number generator is the RandomState instance used
by `np.random`
splitter
"best"
openml-python
python
scikit-learn
sklearn
sklearn_0.22.2
openml-python
python
scikit-learn
sklearn
sklearn_0.22.2