17482
10987
sklearn.pipeline.Pipeline(onehot=sklearn.preprocessing._encoders.OneHotEncoder,scaler=sklearn.preprocessing._data.StandardScaler,classifier=sklearn.neighbors._classification.KNeighborsClassifier)
sklearn.Pipeline(OneHotEncoder,StandardScaler,KNeighborsClassifier)
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``.
2020-01-10T19:00:41
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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": "onehot", "step_name": "onehot"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}, {"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.
scaler
17480
10987
sklearn.preprocessing._data.StandardScaler
sklearn.StandardScaler
sklearn.preprocessing._data.StandardScaler
1
openml==0.10.2,sklearn==0.22.1
Standardize features by removing the mean and scaling to unit variance
The standard score of a sample `x` is calculated as:
z = (x - u) / s
where `u` is the mean of the training samples or zero if `with_mean=False`,
and `s` is the standard deviation of the training samples or one if
`with_std=False`.
Centering and scaling happen independently on each feature by computing
the relevant statistics on the samples in the training set. Mean and
standard deviation are then stored to be used on later data using
:meth:`transform`.
Standardization of a dataset is a common requirement for many
machine learning estimators: they might behave badly if the
individual features do not more or less look like standard normally
distributed data (e.g. Gaussian with 0 mean and unit variance).
For instance many elements used in the objective function of
a learning algorithm (such as the RBF kernel of Support Vector
Machines or the L1 and L2 regularizers of linear models) assume that
all features are centered around 0 a...
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sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
copy
boolean
true
If False, try to avoid a copy and do inplace scaling instead
This is not guaranteed to always work inplace; e.g. if the data is
not a NumPy array or scipy.sparse CSR matrix, a copy may still be
returned
with_mean
boolean
true
If True, center the data before scaling
This does not work (and will raise an exception) when attempted on
sparse matrices, because centering them entails building a dense
matrix which in common use cases is likely to be too large to fit in
memory
with_std
boolean
true
If True, scale the data to unit variance (or equivalently,
unit standard deviation).
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
classifier
17481
10987
sklearn.neighbors._classification.KNeighborsClassifier
sklearn.KNeighborsClassifier
sklearn.neighbors._classification.KNeighborsClassifier
2
openml==0.10.2,sklearn==0.22.1
Classifier implementing the k-nearest neighbors vote.
2020-01-10T17:13:24
English
sklearn==0.22.1
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
string 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 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
null
Additional keyword arguments for the metric function
n_jobs
int or None
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
p
integer
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
str or callable
"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
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
onehot
17483
10987
sklearn.preprocessing._encoders.OneHotEncoder
sklearn.OneHotEncoder
sklearn.preprocessing._encoders.OneHotEncoder
19
openml==0.10.2,sklearn==0.22.1
Encode categorical features as a one-hot numeric array.
The input to this transformer should be an array-like of integers or
strings, denoting the values taken on by categorical (discrete) features.
The features are encoded using a one-hot (aka 'one-of-K' or 'dummy')
encoding scheme. This creates a binary column for each category and
returns a sparse matrix or dense array (depending on the ``sparse``
parameter)
By default, the encoder derives the categories based on the unique values
in each feature. Alternatively, you can also specify the `categories`
manually.
This encoding is needed for feeding categorical data to many scikit-learn
estimators, notably linear models and SVMs with the standard kernels.
Note: a one-hot encoding of y labels should use a LabelBinarizer
instead.
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sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
categories
'auto' or a list of array
"auto"
Categories (unique values) per feature:
- 'auto' : Determine categories automatically from the training data
- list : ``categories[i]`` holds the categories expected in the ith
column. The passed categories should not mix strings and numeric
values within a single feature, and should be sorted in case of
numeric values
The used categories can be found in the ``categories_`` attribute
drop
'first' or a array
null
Specifies a methodology to use to drop one of the categories per
feature. This is useful in situations where perfectly collinear
features cause problems, such as when feeding the resulting data
into a neural network or an unregularized regression
dtype
number type
{"oml-python:serialized_object": "type", "value": "np.float64"}
Desired dtype of output
handle_unknown : {'error', 'ignore'}, default='error'
Whether to raise an error or ignore if an unknown categorical feature
is present during transform (default is to raise). When this parameter
is set to 'ignore' and an unknown category is encountered during
transform, the resulting one-hot encoded columns for this feature
will be all zeros. In the inverse transform, an unknown category
will be denoted as None.
handle_unknown
"error"
sparse
bool
true
Will return sparse matrix if set True else will return an array
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1