675
1159
TEST9287f909e5sklearn.pipeline.Pipeline(scaler=sklearn.preprocessing._data.StandardScaler,dummy=sklearn.dummy.DummyClassifier)
sklearn.Pipeline(StandardScaler,DummyClassifier)
sklearn.pipeline.Pipeline
1
openml==0.15.0,sklearn==0.23.1
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``.
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sklearn==0.23.1
numpy>=1.13.3
scipy>=0.19.1
joblib>=0.11
threadpoolctl>=2.0.0
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": "scaler", "step_name": "scaler"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "dummy", "step_name": "dummy"}}]
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
676
1159
TEST9287f909e5sklearn.preprocessing._data.StandardScaler
sklearn.StandardScaler
sklearn.preprocessing._data.StandardScaler
1
openml==0.15.0,sklearn==0.23.1
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==0.23.1
numpy>=1.13.3
scipy>=0.19.1
joblib>=0.11
threadpoolctl>=2.0.0
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
false
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_0.23.1
dummy
677
1159
TEST9287f909e5sklearn.dummy.DummyClassifier
sklearn.DummyClassifier
sklearn.dummy.DummyClassifier
1
openml==0.15.0,sklearn==0.23.1
DummyClassifier is a classifier that makes predictions using simple rules.
This classifier is useful as a simple baseline to compare with other
(real) classifiers. Do not use it for real problems.
2024-10-17T13:55:33
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sklearn==0.23.1
numpy>=1.13.3
scipy>=0.19.1
joblib>=0.11
threadpoolctl>=2.0.0
constant
int or str or array
null
The explicit constant as predicted by the "constant" strategy. This
parameter is useful only for the "constant" strategy.
random_state
int
null
Controls the randomness to generate the predictions when
``strategy='stratified'`` or ``strategy='uniform'``
Pass an int for reproducible output across multiple function calls
See :term:`Glossary <random_state>`
strategy
str
"prior"
Strategy to use to generate predictions
* "stratified": generates predictions by respecting the training
set's class distribution
* "most_frequent": always predicts the most frequent label in the
training set
* "prior": always predicts the class that maximizes the class prior
(like "most_frequent") and ``predict_proba`` returns the class prior
* "uniform": generates predictions uniformly at random
* "constant": always predicts a constant label that is provided by
the user. This is useful for metrics that evaluate a non-majority
class
.. versionchanged:: 0.22
The default value of `strategy` will change to "prior" in version
0.24. Starting from version 0.22, a warning will be raised if
`strategy` is not explicitly set
.. versionadded:: 0.17
Dummy Classifier now supports prior fitting strategy using
parameter *prior*
openml-python
python
scikit-learn
sklearn
sklearn_0.23.1
openml-python
python
scikit-learn
sklearn
sklearn_0.23.1