18462
12269
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.decomposition._kernel_pca.KernelPCA,step_2=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)
sklearn.Pipeline(MissingIndicator,KernelPCA,LinearDiscriminantAnalysis)
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``.
2020-05-21T07:57:59
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
17705
12269
sklearn.discriminant_analysis.LinearDiscriminantAnalysis
sklearn.LinearDiscriminantAnalysis
sklearn.discriminant_analysis.LinearDiscriminantAnalysis
4
openml==0.10.2,sklearn==0.22.1
Linear Discriminant Analysis
A classifier with a linear decision boundary, generated by fitting class
conditional densities to the data and using Bayes' rule.
The model fits a Gaussian density to each class, assuming that all classes
share the same covariance matrix.
The fitted model can also be used to reduce the dimensionality of the input
by projecting it to the most discriminative directions.
.. versionadded:: 0.17
*LinearDiscriminantAnalysis*.
2020-05-18T19:46:37
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
n_components
int
346
Number of components (<= min(n_classes - 1, n_features)) for
dimensionality reduction. If None, will be set to
min(n_classes - 1, n_features)
priors
array
null
Class priors
shrinkage
string or float
0.4904334135320484
Shrinkage parameter, possible values:
- None: no shrinkage (default)
- 'auto': automatic shrinkage using the Ledoit-Wolf lemma
- float between 0 and 1: fixed shrinkage parameter
Note that shrinkage works only with 'lsqr' and 'eigen' solvers
solver
string
"eigen"
Solver to use, possible values:
- 'svd': Singular value decomposition (default)
Does not compute the covariance matrix, therefore this solver is
recommended for data with a large number of features
- 'lsqr': Least squares solution, can be combined with shrinkage
- 'eigen': Eigenvalue decomposition, can be combined with shrinkage
store_covariance
bool
false
Additionally compute class covariance matrix (default False), used
only in 'svd' solver
.. versionadded:: 0.17
tol
float
0.0012994132677570124
Threshold used for rank estimation in SVD solver
.. versionadded:: 0.17
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