17792
12269
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.PolynomialFeatures,step_1=sklearn.linear_model._stochastic_gradient.SGDClassifier)
sklearn.Pipeline(PolynomialFeatures,SGDClassifier)
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``.
2020-05-19T00:05:07
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
17703
12269
sklearn.linear_model._stochastic_gradient.SGDClassifier
sklearn.SGDClassifier
sklearn.linear_model._stochastic_gradient.SGDClassifier
2
openml==0.10.2,sklearn==0.22.1
Linear classifiers (SVM, logistic regression, a.o.) with SGD training.
This estimator implements regularized linear models with stochastic
gradient descent (SGD) learning: the gradient of the loss is estimated
each sample at a time and the model is updated along the way with a
decreasing strength schedule (aka learning rate). SGD allows minibatch
(online/out-of-core) learning, see the partial_fit method.
For best results using the default learning rate schedule, the data should
have zero mean and unit variance.
This implementation works with data represented as dense or sparse arrays
of floating point values for the features. The model it fits can be
controlled with the loss parameter; by default, it fits a linear support
vector machine (SVM).
The regularizer is a penalty added to the loss function that shrinks model
parameters towards the zero vector using either the squared euclidean norm
L2 or the absolute norm L1 or a combination of both (Elastic Net). If the
parameter update crosses the 0.0 value b...
2020-05-18T19:46:26
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
alpha
float
18.576489600940455
Constant that multiplies the regularization term. Defaults to 0.0001
Also used to compute learning_rate when set to 'optimal'
average
bool or int
false
When set to True, computes the averaged SGD weights and stores the
result in the ``coef_`` attribute. If set to an int greater than 1,
averaging will begin once the total number of samples seen reaches
average. So ``average=10`` will begin averaging after seeing 10
samples.
class_weight
dict
null
Preset for the class_weight fit parameter
Weights associated with classes. If not given, all classes
are supposed to have weight one
The "balanced" mode uses the values of y to automatically adjust
weights inversely proportional to class frequencies in the input data
as ``n_samples / (n_classes * np.bincount(y))``
early_stopping
bool
true
Whether to use early stopping to terminate training when validation
score is not improving. If set to True, it will automatically set aside
a stratified fraction of training data as validation and terminate
training when validation score is not improving by at least tol for
n_iter_no_change consecutive epochs
.. versionadded:: 0.20
epsilon
float
0.9292126060861614
Epsilon in the epsilon-insensitive loss functions; only if `loss` is
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'
For 'huber', determines the threshold at which it becomes less
important to get the prediction exactly right
For epsilon-insensitive, any differences between the current prediction
and the correct label are ignored if they are less than this threshold
eta0
double
0.8230008156002588
The initial learning rate for the 'constant', 'invscaling' or
'adaptive' schedules. The default value is 0.0 as eta0 is not used by
the default schedule 'optimal'
fit_intercept
bool
false
Whether the intercept should be estimated or not. If False, the
data is assumed to be already centered. Defaults to True
l1_ratio
float
0.3888555696189441
The Elastic Net mixing parameter, with 0 <= l1_ratio <= 1
l1_ratio=0 corresponds to L2 penalty, l1_ratio=1 to L1
Defaults to 0.15
learning_rate
str
"constant"
The learning rate schedule:
'constant':
eta = eta0
'optimal': [default]
eta = 1.0 / (alpha * (t + t0))
where t0 is chosen by a heuristic proposed by Leon Bottou
'invscaling':
eta = eta0 / pow(t, power_t)
'adaptive':
eta = eta0, as long as the training keeps decreasing
Each time n_iter_no_change consecutive epochs fail to decrease the
training loss by tol or fail to increase validation score by tol if
early_stopping is True, the current learning rate is divided by 5
loss
str
"modified_huber"
The loss function to be used. Defaults to 'hinge', which gives a
linear SVM
The possible options are 'hinge', 'log', 'modified_huber',
'squared_hinge', 'perceptron', or a regression loss: 'squared_loss',
'huber', 'epsilon_insensitive', or 'squared_epsilon_insensitive'
The 'log' loss gives logistic regression, a probabilistic classifier
'modified_huber' is another smooth loss that brings tolerance to
outliers as well as probability estimates
'squared_hinge' is like hinge but is quadratically penalized
'perceptron' is the linear loss used by the perceptron algorithm
The other losses are designed for regression but can be useful in
classification as well; see SGDRegressor for a description
penalty : {'l2', 'l1', 'elasticnet'}, default='l2'
The penalty (aka regularization term) to be used. Defaults to 'l2'
which is the standard regularizer for linear SVM models. 'l1' and
'elasticnet' might bring sparsity to the model (feature selection)
not achieva...
max_iter
int
1657
The maximum number of passes over the training data (aka epochs)
It only impacts the behavior in the ``fit`` method, and not the
:meth:`partial_fit` method
.. versionadded:: 0.19
n_iter_no_change
int
44
Number of iterations with no improvement to wait before early stopping
.. versionadded:: 0.20
n_jobs
int
1
The number of CPUs to use to do the OVA (One Versus All, for
multi-class problems) computation
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details
penalty
"elasticnet"
power_t
double
0.5337479304733836
The exponent for inverse scaling learning rate [default 0.5]
random_state
int
42
The seed of the pseudo random number generator to use when shuffling
the data. 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
bool
true
Whether or not the training data should be shuffled after each epoch
tol
float
0.0016762319789051909
The stopping criterion. If it is not None, the iterations will stop
when (loss > best_loss - tol) for ``n_iter_no_change`` consecutive
epochs
.. versionadded:: 0.19
validation_fraction
float
0.5789400632046087
The proportion of training data to set aside as validation set for
early stopping. Must be between 0 and 1
Only used if early_stopping is True
.. versionadded:: 0.20
verbose
int
0
The verbosity level
warm_start
bool
false
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution
See :term:`the Glossary <warm_start>`
Repeatedly calling fit or partial_fit when warm_start is True can
result in a different solution than when calling fit a single time
because of the way the data is shuffled
If a dynamic learning rate is used, the learning rate is adapted
depending on the number of samples already seen. Calling ``fit`` resets
this counter, while ``partial_fit`` will result in increasing the
existing counter
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
step_0
17749
12269
sklearn.preprocessing._data.PolynomialFeatures
sklearn.PolynomialFeatures
sklearn.preprocessing._data.PolynomialFeatures
1
openml==0.10.2,sklearn==0.22.1
Generate polynomial and interaction features.
Generate a new feature matrix consisting of all polynomial combinations
of the features with degree less than or equal to the specified degree.
For example, if an input sample is two dimensional and of the form
[a, b], the degree-2 polynomial features are [1, a, b, a^2, ab, b^2].
2020-05-18T23:49:43
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
degree
integer
2
The degree of the polynomial features. Default = 2
include_bias
boolean
false
If True (default), then include a bias column, the feature in which
all polynomial powers are zero (i.e. a column of ones - acts as an
intercept term in a linear model)
interaction_only
boolean
false
If true, only interaction features are produced: features that are
products of at most ``degree`` *distinct* input features (so not
``x[1] ** 2``, ``x[0] * x[2] ** 3``, etc.)
order
str in
"C"
Order of output array in the dense case. 'F' order is faster to
compute, but may slow down subsequent estimators
.. versionadded:: 0.21
Examples
--------
>>> import numpy as np
>>> from sklearn.preprocessing import PolynomialFeatures
>>> X = np.arange(6).reshape(3, 2)
>>> X
array([[0, 1],
[2, 3],
[4, 5]])
>>> poly = PolynomialFeatures(2)
>>> poly.fit_transform(X)
array([[ 1., 0., 1., 0., 0., 1.],
[ 1., 2., 3., 4., 6., 9.],
[ 1., 4., 5., 16., 20., 25.]])
>>> poly = PolynomialFeatures(interaction_only=True)
>>> poly.fit_transform(X)
array([[ 1., 0., 1., 0.],
[ 1., 2., 3., 6.],
[ 1., 4., 5., 20.]])
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1