18428
12269
sklearn.pipeline.Pipeline(step_0=automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent,step_1=sklearn.preprocessing._discretization.KBinsDiscretizer,step_2=sklearn.ensemble._weight_boosting.AdaBoostClassifier)
sklearn.Pipeline(MultiColumnLabelEncoderComponent,KBinsDiscretizer,AdaBoostClassifier)
sklearn.pipeline.Pipeline
1
automl==0.0.1,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``.
2020-05-20T18:19:46
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"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_2", "step_name": "step_2"}}]
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_2
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].
2020-05-18T19:37:50
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
17711
12269
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent
automl.MultiColumnLabelEncoderComponent
automl.components.feature_preprocessing.multi_column_label_encoder.MultiColumnLabelEncoderComponent
1
automl==0.0.1,openml==0.10.2,sklearn==0.22.1
MultiColumnLabelEncoderComponent
A ColumnEncoder that can handle missing values and multiple categorical columns.
2020-05-18T21:55:00
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
columns
List
null
List of column to be encoded
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
step_1
17785
12269
sklearn.preprocessing._discretization.KBinsDiscretizer
sklearn.KBinsDiscretizer
sklearn.preprocessing._discretization.KBinsDiscretizer
1
openml==0.10.2,sklearn==0.22.1
Bin continuous data into intervals.
2020-05-19T00:02:40
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
encode
"onehot"
n_bins
int or array
2
The number of bins to produce. Raises ValueError if ``n_bins < 2``
encode : {'onehot', 'onehot-dense', 'ordinal'}, (default='onehot')
Method used to encode the transformed result
onehot
Encode the transformed result with one-hot encoding
and return a sparse matrix. Ignored features are always
stacked to the right
onehot-dense
Encode the transformed result with one-hot encoding
and return a dense array. Ignored features are always
stacked to the right
ordinal
Return the bin identifier encoded as an integer value
strategy : {'uniform', 'quantile', 'kmeans'}, (default='quantile')
Strategy used to define the widths of the bins
uniform
All bins in each feature have identical widths
quantile
All bins in each feature have the same number of points
kmeans
Values in each bin have the same nearest center of a 1D k-means
cluster.
strategy
"uniform"
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1