18699
18203
sklearn.pipeline.Pipeline(preprocessor=sklearn.compose._column_transformer.ColumnTransformer,classifier=sklearn.neighbors._classification.KNeighborsClassifier)
sklearn.Pipeline(ColumnTransformer,KNeighborsClassifier)
sklearn.pipeline.Pipeline
1
openml==0.10.2,sklearn==0.23.2
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``.
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English
sklearn==0.23.2
numpy>=1.6.1
scipy>=0.9
memory
str or object with the joblib
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": "preprocessor", "step_name": "preprocessor"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
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.
preprocessor
18696
18203
sklearn.compose._column_transformer.ColumnTransformer
sklearn.ColumnTransformer
sklearn.compose._column_transformer.ColumnTransformer
1
openml==0.10.2,sklearn==0.23.2
Applies transformers to columns of an array or pandas DataFrame.
This estimator allows different columns or column subsets of the input
to be transformed separately and the features generated by each transformer
will be concatenated to form a single feature space.
This is useful for heterogeneous or columnar data, to combine several
feature extraction mechanisms or transformations into a single transformer.
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sklearn==0.23.2
numpy>=1.6.1
scipy>=0.9
n_jobs
int
null
Number of jobs to run in parallel
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details
remainder
"drop"
sparse_threshold
float
0.3
If the output of the different transformers contains sparse matrices,
these will be stacked as a sparse matrix if the overall density is
lower than this value. Use ``sparse_threshold=0`` to always return
dense. When the transformed output consists of all dense data, the
stacked result will be dense, and this keyword will be ignored
transformer_weights
dict
null
Multiplicative weights for features per transformer. The output of the
transformer is multiplied by these weights. Keys are transformer names,
values the weights
transformers
list of tuples
[["selection", "passthrough", [0, 1]]]
List of (name, transformer, columns) tuples specifying the
transformer objects to be applied to subsets of the data
verbose
bool
false
If True, the time elapsed while fitting each transformer will be
printed as it is completed.
openml-python
python
scikit-learn
sklearn
sklearn_0.23.2
classifier
18697
18203
sklearn.neighbors._classification.KNeighborsClassifier
sklearn.KNeighborsClassifier
sklearn.neighbors._classification.KNeighborsClassifier
5
openml==0.10.2,sklearn==0.23.2
Classifier implementing the k-nearest neighbors vote.
2020-10-11T03:54:35
English
sklearn==0.23.2
numpy>=1.6.1
scipy>=0.9
algorithm
"auto"
leaf_size
int
30
Leaf size passed to BallTree or KDTree. This can affect the
speed of the construction and query, as well as the memory
required to store the tree. The optimal value depends on the
nature of the problem
metric
str or callable
"minkowski"
the distance metric to use for the tree. The default metric is
minkowski, and with p=2 is equivalent to the standard Euclidean
metric. See the documentation of :class:`DistanceMetric` for a
list of available metrics
If metric is "precomputed", X is assumed to be a distance matrix and
must be square during fit. X may be a :term:`sparse graph`,
in which case only "nonzero" elements may be considered neighbors
metric_params
dict
null
Additional keyword arguments for the metric function
n_jobs
int
null
The number of parallel jobs to run for neighbors search
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details
Doesn't affect :meth:`fit` method.
n_neighbors
int
5
Number of neighbors to use by default for :meth:`kneighbors` queries
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
- [callable] : a user-defined function which accepts an
array of distances, and returns an array of the same shape
containing the weights
algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, default='auto'
Algorithm used to compute the nearest neighbors:
- 'ball_tree' will use :class:`BallTree`
- 'kd_tree' will use :class:`KDTree`
- 'brute' will use a brute-force search
- 'auto' will attempt to decide the most appropriate algorithm
based on the values passed to :meth:`fit` method
No...
p
int
2
Power parameter for the Minkowski metric. When p = 1, this is
equivalent to using manhattan_distance (l1), and euclidean_distance
(l2) for p = 2. For arbitrary p, minkowski_distance (l_p) is used
weights
"uniform"
openml-python
python
scikit-learn
sklearn
sklearn_0.23.2
openml-python
python
scikit-learn
sklearn
sklearn_0.23.2