17750
12269
sklearn.pipeline.Pipeline(step_0=sklearn.decomposition._factor_analysis.FactorAnalysis,step_1=sklearn.ensemble._weight_boosting.AdaBoostClassifier)
sklearn.Pipeline(FactorAnalysis,AdaBoostClassifier)
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``.
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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
17696
12269
sklearn.ensemble._weight_boosting.AdaBoostClassifier
sklearn.AdaBoostClassifier
sklearn.ensemble._weight_boosting.AdaBoostClassifier
2
openml==0.10.2,sklearn==0.22.1
An AdaBoost classifier.
An AdaBoost [1] classifier is a meta-estimator that begins by fitting a
classifier on the original dataset and then fits additional copies of the
classifier on the same dataset but where the weights of incorrectly
classified instances are adjusted such that subsequent classifiers focus
more on difficult cases.
This class implements the algorithm known as AdaBoost-SAMME [2].
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English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
algorithm
"SAMME"
base_estimator
object
null
The base estimator from which the boosted ensemble is built
Support for sample weighting is required, as well as proper
``classes_`` and ``n_classes_`` attributes. If ``None``, then
the base estimator is ``DecisionTreeClassifier(max_depth=1)``
learning_rate
float
1.5568079200489067e-05
Learning rate shrinks the contribution of each classifier by
``learning_rate``. There is a trade-off between ``learning_rate`` and
``n_estimators``
algorithm : {'SAMME', 'SAMME.R'}, optional (default='SAMME.R')
If 'SAMME.R' then use the SAMME.R real boosting algorithm
``base_estimator`` must support calculation of class probabilities
If 'SAMME' then use the SAMME discrete boosting algorithm
The SAMME.R algorithm typically converges faster than SAMME,
achieving a lower test error with fewer boosting iterations
n_estimators
int
532
The maximum number of estimators at which boosting is terminated
In case of perfect fit, the learning procedure is stopped early
random_state
int
42
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`.
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
step_0
17734
12269
sklearn.decomposition._factor_analysis.FactorAnalysis
sklearn.FactorAnalysis
sklearn.decomposition._factor_analysis.FactorAnalysis
1
openml==0.10.2,sklearn==0.22.1
Factor Analysis (FA)
A simple linear generative model with Gaussian latent variables.
The observations are assumed to be caused by a linear transformation of
lower dimensional latent factors and added Gaussian noise.
Without loss of generality the factors are distributed according to a
Gaussian with zero mean and unit covariance. The noise is also zero mean
and has an arbitrary diagonal covariance matrix.
If we would restrict the model further, by assuming that the Gaussian
noise is even isotropic (all diagonal entries are the same) we would obtain
:class:`PPCA`.
FactorAnalysis performs a maximum likelihood estimate of the so-called
`loading` matrix, the transformation of the latent variables to the
observed ones, using SVD based approach.
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English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
copy
bool
false
Whether to make a copy of X. If ``False``, the input X gets overwritten
during fitting
iterated_power
int
3
Number of iterations for the power method. 3 by default. Only used
if ``svd_method`` equals 'randomized'
max_iter
int
7723
Maximum number of iterations
n_components
int
3
Dimensionality of latent space, the number of components
of ``X`` that are obtained after ``transform``
If None, n_components is set to the number of features
noise_variance_init
None
null
The initial guess of the noise variance for each feature
If None, it defaults to np.ones(n_features)
svd_method : {'lapack', 'randomized'}
Which SVD method to use. If 'lapack' use standard SVD from
scipy.linalg, if 'randomized' use fast ``randomized_svd`` function
Defaults to 'randomized'. For most applications 'randomized' will
be sufficiently precise while providing significant speed gains
Accuracy can also be improved by setting higher values for
`iterated_power`. If this is not sufficient, for maximum precision
you should choose 'lapack'
random_state
int
42
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`. Only used when ``svd_method`` equals 'randomized'.
svd_method
"lapack"
tol
float
1.841563435236402
Stopping tolerance for log-likelihood increase
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1