17786
12269
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._kernel_pca.KernelPCA,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)
sklearn.Pipeline(KernelPCA,AdaBoostClassifier)
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``.
2020-05-19T00:02:57
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"}}]
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_1
17696
12269
sklearn.ensemble._weight_boosting.AdaBoostClassifier
sklearn.AdaBoostClassifier
sklearn.ensemble._weight_boosting.AdaBoostClassifier
2
openml==0.10.2,sklearn==0.22.1
An AdaBoost classifier.
An AdaBoost [1] classifier is a meta-estimator that begins by fitting a
classifier on the original dataset and then fits additional copies of the
classifier on the same dataset but where the weights of incorrectly
classified instances are adjusted such that subsequent classifiers focus
more on difficult cases.
This class implements the algorithm known as AdaBoost-SAMME [2].
2020-05-18T19:37:50
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
algorithm
"SAMME"
base_estimator
object
null
The base estimator from which the boosted ensemble is built
Support for sample weighting is required, as well as proper
``classes_`` and ``n_classes_`` attributes. If ``None``, then
the base estimator is ``DecisionTreeClassifier(max_depth=1)``
learning_rate
float
1.5568079200489067e-05
Learning rate shrinks the contribution of each classifier by
``learning_rate``. There is a trade-off between ``learning_rate`` and
``n_estimators``
algorithm : {'SAMME', 'SAMME.R'}, optional (default='SAMME.R')
If 'SAMME.R' then use the SAMME.R real boosting algorithm
``base_estimator`` must support calculation of class probabilities
If 'SAMME' then use the SAMME discrete boosting algorithm
The SAMME.R algorithm typically converges faster than SAMME,
achieving a lower test error with fewer boosting iterations
n_estimators
int
532
The maximum number of estimators at which boosting is terminated
In case of perfect fit, the learning procedure is stopped early
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`.
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
step_0
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
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1