18466
12269
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._kernel_pca.KernelPCA,step_2=sklearn.tree._classes.DecisionTreeClassifier)
sklearn.Pipeline(MissingIndicator,KernelPCA,DecisionTreeClassifier)
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``.
2020-05-21T08:02:54
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
17504
11295
sklearn.tree._classes.DecisionTreeClassifier
sklearn.DecisionTreeClassifier
sklearn.tree._classes.DecisionTreeClassifier
3
openml==0.10.2,sklearn==0.22.1
A decision tree classifier.
2020-02-08T19:46:35
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
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
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
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_0.22.1
step_1
17740
12269
sklearn.decomposition._kernel_pca.KernelPCA
sklearn.KernelPCA
sklearn.decomposition._kernel_pca.KernelPCA
1
openml==0.10.2,sklearn==0.22.1
Kernel Principal component analysis (KPCA)
Non-linear dimensionality reduction through the use of kernels (see
:ref:`metrics`).
2020-05-18T23:48:33
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
alpha
int
4
Hyperparameter of the ridge regression that learns the
inverse transform (when fit_inverse_transform=True)
coef0
float
1
Independent term in poly and sigmoid kernels
Ignored by other kernels
copy_X
boolean
false
If True, input X is copied and stored by the model in the `X_fit_`
attribute. If no further changes will be done to X, setting
`copy_X=False` saves memory by storing a reference
.. versionadded:: 0.18
degree
int
3
Degree for poly kernels. Ignored by other kernels
eigen_solver
string
"arpack"
Select eigensolver to use. If n_components is much less than
the number of training samples, arpack may be more efficient
than the dense eigensolver
fit_inverse_transform
bool
true
Learn the inverse transform for non-precomputed kernels
(i.e. learn to find the pre-image of a point)
gamma
float
null
Kernel coefficient for rbf, poly and sigmoid kernels. Ignored by other
kernels
kernel
"cosine"
kernel_params
mapping of string to any
null
Parameters (keyword arguments) and values for kernel passed as
callable object. Ignored by other kernels
max_iter
int
524
Maximum number of iterations for arpack
If None, optimal value will be chosen by arpack
n_components
int
4
Number of components. If None, all non-zero components are kept
kernel : "linear" | "poly" | "rbf" | "sigmoid" | "cosine" | "precomputed"
Kernel. Default="linear"
n_jobs
int or None
1
The number of parallel jobs to run
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details
.. versionadded:: 0.18
random_state
int
42
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`. Used when ``eigen_solver`` == 'arpack'
.. versionadded:: 0.18
remove_zero_eig
boolean
false
If True, then all components with zero eigenvalues are removed, so
that the number of components in the output may be < n_components
(and sometimes even zero due to numerical instability)
When n_components is None, this parameter is ignored and components
with zero eigenvalues are removed regardless
tol
float
0.6476998368285369
Convergence tolerance for arpack
If 0, optimal value will be chosen by arpack
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