17970
12269
sklearn.pipeline.Pipeline(step_0=automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier),step_1=sklearn.discriminant_analysis.LinearDiscriminantAnalysis)
sklearn.Pipeline(StackingEstimator,LinearDiscriminantAnalysis)
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``.
2020-05-19T06:05:28
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
17705
12269
sklearn.discriminant_analysis.LinearDiscriminantAnalysis
sklearn.LinearDiscriminantAnalysis
sklearn.discriminant_analysis.LinearDiscriminantAnalysis
4
openml==0.10.2,sklearn==0.22.1
Linear Discriminant Analysis
A classifier with a linear decision boundary, generated by fitting class
conditional densities to the data and using Bayes' rule.
The model fits a Gaussian density to each class, assuming that all classes
share the same covariance matrix.
The fitted model can also be used to reduce the dimensionality of the input
by projecting it to the most discriminative directions.
.. versionadded:: 0.17
*LinearDiscriminantAnalysis*.
2020-05-18T19:46:37
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
n_components
int
346
Number of components (<= min(n_classes - 1, n_features)) for
dimensionality reduction. If None, will be set to
min(n_classes - 1, n_features)
priors
array
null
Class priors
shrinkage
string or float
0.4904334135320484
Shrinkage parameter, possible values:
- None: no shrinkage (default)
- 'auto': automatic shrinkage using the Ledoit-Wolf lemma
- float between 0 and 1: fixed shrinkage parameter
Note that shrinkage works only with 'lsqr' and 'eigen' solvers
solver
string
"eigen"
Solver to use, possible values:
- 'svd': Singular value decomposition (default)
Does not compute the covariance matrix, therefore this solver is
recommended for data with a large number of features
- 'lsqr': Least squares solution, can be combined with shrinkage
- 'eigen': Eigenvalue decomposition, can be combined with shrinkage
store_covariance
bool
false
Additionally compute class covariance matrix (default False), used
only in 'svd' solver
.. versionadded:: 0.17
tol
float
0.0012994132677570124
Threshold used for rank estimation in SVD solver
.. versionadded:: 0.17
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
step_0
17924
12269
automl.util.sklearn.StackingEstimator(estimator=sklearn.tree._classes.DecisionTreeClassifier)
automl.StackingEstimator
automl.util.sklearn.StackingEstimator
1
automl==0.0.1,openml==0.10.2,sklearn==0.22.1
StackingEstimator
A shallow wrapper around a classification algorithm to implement the transform method. Allows stacking of
arbitrary classification algorithms in a pipelines.
2020-05-19T04:03:39
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
estimator
PredictionMixin
{"oml-python:serialized_object": "component_reference", "value": {"key": "estimator", "step_name": null}}
An instance implementing PredictionMixin
estimator
17504
11295
sklearn.tree._classes.DecisionTreeClassifier
sklearn.DecisionTreeClassifier
sklearn.tree._classes.DecisionTreeClassifier
3
openml==0.10.2,sklearn==0.22.1
A decision tree classifier.
2020-02-08T19:46:35
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
ccp_alpha
non
0.0
Complexity parameter used for Minimal Cost-Complexity Pruning. The
subtree with the largest cost complexity that is smaller than
``ccp_alpha`` will be chosen. By default, no pruning is performed. See
:ref:`minimal_cost_complexity_pruning` for details
.. versionadded:: 0.22
class_weight
dict
null
Weights associated with classes in the form ``{class_label: weight}``
If None, all classes are supposed to have weight one. For
multi-output problems, a list of dicts can be provided in the same
order as the columns of y
Note that for multioutput (including multilabel) weights should be
defined for each class of every column in its own dict. For example,
for four-class multilabel classification weights should be
[{0: 1, 1: 1}, {0: 1, 1: 5}, {0: 1, 1: 1}, {0: 1, 1: 1}] instead of
[{1:1}, {2:5}, {3:1}, {4:1}]
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))``
For multi-output, the weights of each column of y will be multiplied
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified
criterion
"gini"
max_depth
int
null
The maximum depth of the tree. If None, then nodes are expanded until
all leaves are pure or until all leaves contain less than
min_samples_split samples
max_features
int
null
The number of features to consider when looking for the best split:
- If int, then consider `max_features` features at each split
- If float, then `max_features` is a fraction and
`int(max_features * n_features)` features are considered at each
split
- If "auto", then `max_features=sqrt(n_features)`
- If "sqrt", then `max_features=sqrt(n_features)`
- If "log2", then `max_features=log2(n_features)`
- If None, then `max_features=n_features`
Note: the search for a split does not stop until at least one
valid partition of the node samples is found, even if it requires to
effectively inspect more than ``max_features`` features
max_leaf_nodes
int
null
Grow a tree with ``max_leaf_nodes`` in best-first fashion
Best nodes are defined as relative reduction in impurity
If None then unlimited number of leaf nodes
min_impurity_decrease
float
0.0
A node will be split if this split induces a decrease of the impurity
greater than or equal to this value
The weighted impurity decrease equation is the following::
N_t / N * (impurity - N_t_R / N_t * right_impurity
- N_t_L / N_t * left_impurity)
where ``N`` is the total number of samples, ``N_t`` is the number of
samples at the current node, ``N_t_L`` is the number of samples in the
left child, and ``N_t_R`` is the number of samples in the right child
``N``, ``N_t``, ``N_t_R`` and ``N_t_L`` all refer to the weighted sum,
if ``sample_weight`` is passed
.. versionadded:: 0.19
min_impurity_split
float
null
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf
.. deprecated:: 0.19
``min_impurity_split`` has been deprecated in favor of
``min_impurity_decrease`` in 0.19. The default value of
``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it
will be removed in 0.25. Use ``min_impurity_decrease`` instead
min_samples_leaf
int or float
1
The minimum number of samples required to be at a leaf node
A split point at any depth will only be considered if it leaves at
least ``min_samples_leaf`` training samples in each of the left and
right branches. This may have the effect of smoothing the model,
especially in regression
- If int, then consider `min_samples_leaf` as the minimum number
- If float, then `min_samples_leaf` is a fraction and
`ceil(min_samples_leaf * n_samples)` are the minimum
number of samples for each node
.. versionchanged:: 0.18
Added float values for fractions
min_samples_split
int or float
2
The minimum number of samples required to split an internal node:
- If int, then consider `min_samples_split` as the minimum number
- If float, then `min_samples_split` is a fraction and
`ceil(min_samples_split * n_samples)` are the minimum
number of samples for each split
.. versionchanged:: 0.18
Added float values for fractions
min_weight_fraction_leaf
float
0.0
The minimum weighted fraction of the sum total of weights (of all
the input samples) required to be at a leaf node. Samples have
equal weight when sample_weight is not provided
presort
deprecated
"deprecated"
This parameter is deprecated and will be removed in v0.24
.. deprecated:: 0.22
random_state
int or RandomState
null
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`
splitter
"best"
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1