18474
12269
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.preprocessing._data.PolynomialFeatures,step_2=sklearn.ensemble._forest.RandomForestClassifier)
sklearn.Pipeline(MissingIndicator,PolynomialFeatures,RandomForestClassifier)
sklearn.pipeline.Pipeline
1
openml==0.10.2,sklearn==0.22.1
Pipeline of transforms with a final estimator.
Sequentially apply a list of transforms and a final estimator.
Intermediate steps of the pipeline must be 'transforms', that is, they
must implement fit and transform methods.
The final estimator only needs to implement fit.
The transformers in the pipeline can be cached using ``memory`` argument.
The purpose of the pipeline is to assemble several steps that can be
cross-validated together while setting different parameters.
For this, it enables setting parameters of the various steps using their
names and the parameter name separated by a '__', as in the example below.
A step's estimator may be replaced entirely by setting the parameter
with its name to another estimator, or a transformer removed by setting
it to 'passthrough' or ``None``.
2020-05-21T08:08:04
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
memory
None
null
Used to cache the fitted transformers of the pipeline. By default,
no caching is performed. If a string is given, it is the path to
the caching directory. Enabling caching triggers a clone of
the transformers before fitting. Therefore, the transformer
instance given to the pipeline cannot be inspected
directly. Use the attribute ``named_steps`` or ``steps`` to
inspect estimators within the pipeline. Caching the
transformers is advantageous when fitting is time consuming
steps
list
[{"oml-python:serialized_object": "component_reference", "value": {"key": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
List of (name, transform) tuples (implementing fit/transform) that are
chained, in the order in which they are chained, with the last object
an estimator
verbose
bool
false
If True, the time elapsed while fitting each step will be printed as it
is completed.
step_2
17654
8323
sklearn.ensemble._forest.RandomForestClassifier
sklearn.RandomForestClassifier
sklearn.ensemble._forest.RandomForestClassifier
2
openml==0.10.2,sklearn==0.22.1
A random forest classifier.
A random forest is a meta estimator that fits a number of decision tree
classifiers on various sub-samples of the dataset and uses averaging to
improve the predictive accuracy and control over-fitting.
The sub-sample size is always the same as the original
input sample size but the samples are drawn with replacement if
`bootstrap=True` (default).
2020-04-05T22:13:51
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
bootstrap
boolean
true
Whether bootstrap samples are used when building trees. If False, the
whole datset is used to build each tree
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 not given, 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))``
The "balanced_subsample" mode is the same as "balanced" except that
weights are computed based on the bootstrap sample for every tree
grown
For multi-output, the weights of each column of y will be multiplied
Note that these weights will be multiplied...
criterion
string
"gini"
The function to measure the quality of a split. Supported criteria are
"gini" for the Gini impurity and "entropy" for the information gain
Note: this parameter is tree-specific
max_depth
integer or None
null
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
"auto"
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)` (same as "auto")
- 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 or None
null
Grow trees 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
max_samples
int or float
null
If bootstrap is True, the number of samples to draw from X
to train each base estimator
- If None (default), then draw `X.shape[0]` samples
- If int, then draw `max_samples` samples
- If float, then draw `max_samples * X.shape[0]` samples. Thus,
`max_samples` should be in the interval `(0, 1)`
.. versionadded:: 0.22
min_impurity_decrease
float
1e-07
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
0
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
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
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
n_estimators
integer
10
The number of trees in the forest
.. versionchanged:: 0.22
The default value of ``n_estimators`` changed from 10 to 100
in 0.22
n_jobs
int or None
1
The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,
:meth:`decision_path` and :meth:`apply` are all parallelized over the
trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
context. ``-1`` means using all processors. See :term:`Glossary
<n_jobs>` for more details
oob_score
bool
false
Whether to use out-of-bag samples to estimate
the generalization accuracy
random_state
int
1
Controls both the randomness of the bootstrapping of the samples used
when building trees (if ``bootstrap=True``) and the sampling of the
features to consider when looking for the best split at each node
(if ``max_features < n_features``)
See :term:`Glossary <random_state>` for details
verbose
int
0
Controls the verbosity when fitting and predicting
warm_start
bool
false
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See :term:`the Glossary <warm_start>`
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
step_1
17749
12269
sklearn.preprocessing._data.PolynomialFeatures
sklearn.PolynomialFeatures
sklearn.preprocessing._data.PolynomialFeatures
1
openml==0.10.2,sklearn==0.22.1
Generate polynomial and interaction features.
Generate a new feature matrix consisting of all polynomial combinations
of the features with degree less than or equal to the specified degree.
For example, if an input sample is two dimensional and of the form
[a, b], the degree-2 polynomial features are [1, a, b, a^2, ab, b^2].
2020-05-18T23:49:43
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
degree
integer
2
The degree of the polynomial features. Default = 2
include_bias
boolean
false
If True (default), then include a bias column, the feature in which
all polynomial powers are zero (i.e. a column of ones - acts as an
intercept term in a linear model)
interaction_only
boolean
false
If true, only interaction features are produced: features that are
products of at most ``degree`` *distinct* input features (so not
``x[1] ** 2``, ``x[0] * x[2] ** 3``, etc.)
order
str in
"C"
Order of output array in the dense case. 'F' order is faster to
compute, but may slow down subsequent estimators
.. versionadded:: 0.21
Examples
--------
>>> import numpy as np
>>> from sklearn.preprocessing import PolynomialFeatures
>>> X = np.arange(6).reshape(3, 2)
>>> X
array([[0, 1],
[2, 3],
[4, 5]])
>>> poly = PolynomialFeatures(2)
>>> poly.fit_transform(X)
array([[ 1., 0., 1., 0., 0., 1.],
[ 1., 2., 3., 4., 6., 9.],
[ 1., 4., 5., 16., 20., 25.]])
>>> poly = PolynomialFeatures(interaction_only=True)
>>> poly.fit_transform(X)
array([[ 1., 0., 1., 0.],
[ 1., 2., 3., 6.],
[ 1., 4., 5., 20.]])
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
step_0
18449
12269
sklearn.impute._base.MissingIndicator
sklearn.MissingIndicator
sklearn.impute._base.MissingIndicator
1
openml==0.10.2,sklearn==0.22.1
Binary indicators for missing values.
Note that this component typically should not be used in a vanilla
:class:`Pipeline` consisting of transformers and a classifier, but rather
could be added using a :class:`FeatureUnion` or :class:`ColumnTransformer`.
2020-05-21T07:48:00
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
error_on_new
boolean
true
If True (default), transform will raise an error when there are
features with missing values in transform that have no missing values
in fit. This is applicable only when ``features="missing-only"``.
features
str
"all"
Whether the imputer mask should represent all or a subset of
features
- If "missing-only" (default), the imputer mask will only represent
features containing missing values during fit time
- If "all", the imputer mask will represent all features
missing_values
number
NaN
The placeholder for the missing values. All occurrences of
`missing_values` will be indicated (True in the output array), the
other values will be marked as False
sparse
boolean or
"auto"
Whether the imputer mask format should be sparse or dense
- If "auto" (default), the imputer mask will be of same type as
input
- If True, the imputer mask will be a sparse matrix
- If False, the imputer mask will be a numpy array
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1