from sklearn.model_selection import train_test_split train_test_split?
[0;31mSignature:[0m [0mtrain_test_split[0m[0;34m([0m[0;34m*[0m[0marrays[0m[0;34m,[0m [0;34m**[0m[0moptions[0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m
[0;31mDocstring:[0m
Split arrays or matrices into random train and test subsets
Quick utility that wraps input validation and
``next(ShuffleSplit().split(X, y))`` and application to input data
into a single call for splitting (and optionally subsampling) data in a
oneliner.
Read more in the :ref:`User Guide <cross_validation>`.
Parameters
----------
*arrays : sequence of indexables with same length / shape[0]
Allowed inputs are lists, numpy arrays, scipy-sparse
matrices or pandas dataframes.
test_size : float, int or None, optional (default=None)
If float, should be between 0.0 and 1.0 and represent the proportion
of the dataset to include in the test split. If int, represents the
absolute number of test samples. If None, the value is set to the
complement of the train size. If ``train_size`` is also None, it will
be set to 0.25.
train_size : float, int, or None, (default=None)
If float, should be between 0.0 and 1.0 and represent the
proportion of the dataset to include in the train split. If
int, represents the absolute number of train samples. If None,
the value is automatically set to the complement of the test size.
random_state : int, RandomState instance or None, optional (default=None)
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`.
shuffle : boolean, optional (default=True)
Whether or not to shuffle the data before splitting. If shuffle=False
then stratify must be None.
stratify : array-like or None (default=None)
If not None, data is split in a stratified fashion, using this as
the class labels.
Returns
-------
splitting : list, length=2 * len(arrays)
List containing train-test split of inputs.
.. versionadded:: 0.16
If the input is sparse, the output will be a
``scipy.sparse.csr_matrix``. Else, output type is the same as the
input type.
Examples
--------
>>> import numpy as np
>>> from sklearn.model_selection import train_test_split
>>> X, y = np.arange(10).reshape((5, 2)), range(5)
>>> X
array([[0, 1],
[2, 3],
[4, 5],
[6, 7],
[8, 9]])
>>> list(y)
[0, 1, 2, 3, 4]
>>> X_train, X_test, y_train, y_test = train_test_split(
... X, y, test_size=0.33, random_state=42)
...
>>> X_train
array([[4, 5],
[0, 1],
[6, 7]])
>>> y_train
[2, 0, 3]
>>> X_test
array([[2, 3],
[8, 9]])
>>> y_test
[1, 4]
>>> train_test_split(y, shuffle=False)
[[0, 1, 2], [3, 4]]
[0;31mFile:[0m /Applications/anaconda3/lib/python3.7/site-packages/sklearn/model_selection/_split.py
[0;31mType:[0m function
1
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=1)
from sklearn.neighbors import KNeighborsClassifier KNeighborsClassifier?
[0;31mInit signature:[0m
[0mKNeighborsClassifier[0m[0;34m([0m[0;34m[0m
[0;34m[0m [0mn_neighbors[0m[0;34m=[0m[0;36m5[0m[0;34m,[0m[0;34m[0m
[0;34m[0m [0mweights[0m[0;34m=[0m[0;34m'uniform'[0m[0;34m,[0m[0;34m[0m
[0;34m[0m [0malgorithm[0m[0;34m=[0m[0;34m'auto'[0m[0;34m,[0m[0;34m[0m
[0;34m[0m [0mleaf_size[0m[0;34m=[0m[0;36m30[0m[0;34m,[0m[0;34m[0m
[0;34m[0m [0mp[0m[0;34m=[0m[0;36m2[0m[0;34m,[0m[0;34m[0m
[0;34m[0m [0mmetric[0m[0;34m=[0m[0;34m'minkowski'[0m[0;34m,[0m[0;34m[0m
[0;34m[0m [0mmetric_params[0m[0;34m=[0m[0;32mNone[0m[0;34m,[0m[0;34m[0m
[0;34m[0m [0mn_jobs[0m[0;34m=[0m[0;32mNone[0m[0;34m,[0m[0;34m[0m
[0;34m[0m [0;34m**[0m[0mkwargs[0m[0;34m,[0m[0;34m[0m
[0;34m[0m[0;34m)[0m[0;34m[0m[0;34m[0m[0m
[0;31mDocstring:[0m
Classifier implementing the k-nearest neighbors vote.
Read more in the :ref:`User Guide <classification>`.
Parameters
----------
n_neighbors : int, optional (default = 5)
Number of neighbors to use by default for :meth:`kneighbors` queries.
weights : str or callable, optional (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'}, optional
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.
Note: fitting on sparse input will override the setting of
this parameter, using brute force.
leaf_size : int, optional (default = 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.
p : integer, optional (default = 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.
metric : string or callable, default '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 the DistanceMetric class 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:`Glossary <sparse graph>`,
in which case only "nonzero" elements may be considered neighbors.
metric_params : dict, optional (default = None)
Additional keyword arguments for the metric function.
n_jobs : int or None, optional (default=None)
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.
Attributes
----------
classes_ : array of shape (n_classes,)
Class labels known to the classifier
effective_metric_ : string or callble
The distance metric used. It will be same as the `metric` parameter
or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to
'minkowski' and `p` parameter set to 2.
effective_metric_params_ : dict
Additional keyword arguments for the metric function. For most metrics
will be same with `metric_params` parameter, but may also contain the
`p` parameter value if the `effective_metric_` attribute is set to
'minkowski'.
outputs_2d_ : bool
False when `y`'s shape is (n_samples, ) or (n_samples, 1) during fit
otherwise True.
Examples
--------
>>> X = [[0], [1], [2], [3]]
>>> y = [0, 0, 1, 1]
>>> from sklearn.neighbors import KNeighborsClassifier
>>> neigh = KNeighborsClassifier(n_neighbors=3)
>>> neigh.fit(X, y)
KNeighborsClassifier(...)
>>> print(neigh.predict([[1.1]]))
[0]
>>> print(neigh.predict_proba([[0.9]]))
[[0.66666667 0.33333333]]
See also
--------
RadiusNeighborsClassifier
KNeighborsRegressor
RadiusNeighborsRegressor
NearestNeighbors
Notes
-----
See :ref:`Nearest Neighbors <neighbors>` in the online documentation
for a discussion of the choice of ``algorithm`` and ``leaf_size``.
.. warning::
Regarding the Nearest Neighbors algorithms, if it is found that two
neighbors, neighbor `k+1` and `k`, have identical distances
but different labels, the results will depend on the ordering of the
training data.
https://en.wikipedia.org/wiki/K-nearest_neighbor_algorithm
[0;31mFile:[0m /Applications/anaconda3/lib/python3.7/site-packages/sklearn/neighbors/_classification.py
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