18453
12269
sklearn.pipeline.Pipeline(step_0=sklearn.impute._base.MissingIndicator,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)
sklearn.Pipeline(MissingIndicator,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``.
2020-05-21T07:49:53
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
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*.
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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_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