17797
12269
sklearn.pipeline.Pipeline(step_0=sklearn.impute._knn.KNNImputer,step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)
sklearn.Pipeline(KNNImputer,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``.
2020-05-19T00:07:00
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
17742
12269
sklearn.impute._knn.KNNImputer
sklearn.KNNImputer
sklearn.impute._knn.KNNImputer
1
openml==0.10.2,sklearn==0.22.1
Imputation for completing missing values using k-Nearest Neighbors.
Each sample's missing values are imputed using the mean value from
`n_neighbors` nearest neighbors found in the training set. Two samples are
close if the features that neither is missing are close.
2020-05-18T23:48:37
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
add_indicator
bool
false
If True, a :class:`MissingIndicator` transform will stack onto the
output of the imputer's transform. This allows a predictive estimator
to account for missingness despite imputation. If a feature has no
missing values at fit/train time, the feature won't appear on the
missing indicator even if there are missing values at transform/test
time.
copy
bool
false
If True, a copy of X will be created. If False, imputation will
be done in-place whenever possible
metric
"nan_euclidean"
missing_values
number
NaN
The placeholder for the missing values. All occurrences of
`missing_values` will be imputed
n_neighbors
int
21
Number of neighboring samples to use for imputation
weights : {'uniform', 'distance'} or callable, default='uniform'
Weight function used in prediction. Possible values:
- 'uniform' : uniform weights. All points in each neighborhood are
weighted equally
- 'distance' : weight points by the inverse of their distance
in this case, closer neighbors of a query point will have a
greater influence than neighbors which are further away
weights
"uniform"
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1