19545
37152
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder),variancethreshold=sklearn.feature_selection._variance_threshold.VarianceThreshold,randomforestclassifier=sklearn.ensemble._forest.RandomForestClassifier)
sklearn.Pipeline(ColumnTransformer,VarianceThreshold,RandomForestClassifier)
sklearn.pipeline.Pipeline
2
openml==0.14.1,sklearn==1.2.1
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`.
2023-08-07T10:45:09
English
sklearn==1.2.1
numpy>=1.17.3
scipy>=1.3.2
joblib>=1.1.1
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 of tuple
[{"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
List of (name, transform) tuples (implementing `fit`/`transform`) that
are chained in sequential order. The last transform must be an
estimator
verbose
bool
false
If True, the time elapsed while fitting each step will be printed as it
is completed.
columntransformer
19546
37152
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.preprocessing._data.StandardScaler,nominal=sklearn.preprocessing._encoders.OneHotEncoder)
sklearn.ColumnTransformer
sklearn.compose._column_transformer.ColumnTransformer
2
openml==0.14.1,sklearn==1.2.1
Applies transformers to columns of an array or pandas DataFrame.
This estimator allows different columns or column subsets of the input
to be transformed separately and the features generated by each transformer
will be concatenated to form a single feature space.
This is useful for heterogeneous or columnar data, to combine several
feature extraction mechanisms or transformations into a single transformer.
2023-08-07T10:45:09
English
sklearn==1.2.1
numpy>=1.17.3
scipy>=1.3.2
joblib>=1.1.1
threadpoolctl>=2.0.0
n_jobs
int
null
Number of jobs to run in parallel
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details
remainder
"passthrough"
sparse_threshold
float
0.3
If the output of the different transformers contains sparse matrices,
these will be stacked as a sparse matrix if the overall density is
lower than this value. Use ``sparse_threshold=0`` to always return
dense. When the transformed output consists of all dense data, the
stacked result will be dense, and this keyword will be ignored
transformer_weights
dict
null
Multiplicative weights for features per transformer. The output of the
transformer is multiplied by these weights. Keys are transformer names,
values the weights
transformers
list of tuples
[{"oml-python:serialized_object": "component_reference", "value": {"key": "numeric", "step_name": "numeric", "argument_1": [1, 2]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": [0, 3]}}]
List of (name, transformer, columns) tuples specifying the
transformer objects to be applied to subsets of the data
verbose
bool
false
If True, the time elapsed while fitting each transformer will be
printed as it is completed
verbose_feature_names_out
bool
true
If True, :meth:`get_feature_names_out` will prefix all feature names
with the name of the transformer that generated that feature
If False, :meth:`get_feature_names_out` will not prefix any feature
names and will error if feature names are not unique
.. versionadded:: 1.0
numeric
19547
37152
sklearn.preprocessing._data.StandardScaler
sklearn.StandardScaler
sklearn.preprocessing._data.StandardScaler
18
openml==0.14.1,sklearn==1.2.1
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 ...
2023-08-07T10:45:09
English
sklearn==1.2.1
numpy>=1.17.3
scipy>=1.3.2
joblib>=1.1.1
threadpoolctl>=2.0.0
copy
bool
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
bool
true
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
bool
true
If True, scale the data to unit variance (or equivalently,
unit standard deviation).
openml-python
python
scikit-learn
sklearn
sklearn_1.2.1
nominal
19548
37152
sklearn.preprocessing._encoders.OneHotEncoder
sklearn.OneHotEncoder
sklearn.preprocessing._encoders.OneHotEncoder
49
openml==0.14.1,sklearn==1.2.1
Encode categorical features as a one-hot numeric array.
The input to this transformer should be an array-like of integers or
strings, denoting the values taken on by categorical (discrete) features.
The features are encoded using a one-hot (aka 'one-of-K' or 'dummy')
encoding scheme. This creates a binary column for each category and
returns a sparse matrix or dense array (depending on the ``sparse_output``
parameter)
By default, the encoder derives the categories based on the unique values
in each feature. Alternatively, you can also specify the `categories`
manually.
This encoding is needed for feeding categorical data to many scikit-learn
estimators, notably linear models and SVMs with the standard kernels.
Note: a one-hot encoding of y labels should use a LabelBinarizer
instead.
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sklearn==1.2.1
numpy>=1.17.3
scipy>=1.3.2
joblib>=1.1.1
threadpoolctl>=2.0.0
categories
'auto' or a list of array
"auto"
Categories (unique values) per feature:
- 'auto' : Determine categories automatically from the training data
- list : ``categories[i]`` holds the categories expected in the ith
column. The passed categories should not mix strings and numeric
values within a single feature, and should be sorted in case of
numeric values
The used categories can be found in the ``categories_`` attribute
.. versionadded:: 0.20
drop : {'first', 'if_binary'} or an array-like of shape (n_features,), default=None
Specifies a methodology to use to drop one of the categories per
feature. This is useful in situations where perfectly collinear
features cause problems, such as when feeding the resulting data
into an unregularized linear regression model
However, dropping one category breaks the symmetry of the original
representation and can therefore induce a bias in downstream models,
for instance for penalized linear classification or regression models
drop
null
dtype
number type
{"oml-python:serialized_object": "type", "value": "np.float64"}
Desired dtype of output
handle_unknown : {'error', 'ignore', 'infrequent_if_exist'}, default='error'
Specifies the way unknown categories are handled during :meth:`transform`
- 'error' : Raise an error if an unknown category is present during transform
- 'ignore' : When an unknown category is encountered during
transform, the resulting one-hot encoded columns for this feature
will be all zeros. In the inverse transform, an unknown category
will be denoted as None
- 'infrequent_if_exist' : When an unknown category is encountered
during transform, the resulting one-hot encoded columns for this
feature will map to the infrequent category if it exists. The
infrequent category will be mapped to the last position in the
encoding. During inverse transform, an unknown category will be
mapped to the category denoted `'infrequent'` if it exists. If the
`'infrequent'` category does not exist, then :meth:`transform` and
...
handle_unknown
"ignore"
max_categories
int
null
Specifies an upper limit to the number of output features for each input
feature when considering infrequent categories. If there are infrequent
categories, `max_categories` includes the category representing the
infrequent categories along with the frequent categories. If `None`,
there is no limit to the number of output features
.. versionadded:: 1.1
Read more in the :ref:`User Guide <one_hot_encoder_infrequent_categories>`.
min_frequency
int or float
null
Specifies the minimum frequency below which a category will be
considered infrequent
- If `int`, categories with a smaller cardinality will be considered
infrequent
- If `float`, categories with a smaller cardinality than
`min_frequency * n_samples` will be considered infrequent
.. versionadded:: 1.1
Read more in the :ref:`User Guide <one_hot_encoder_infrequent_categories>`
sparse
bool
"deprecated"
Will return sparse matrix if set True else will return an array
.. deprecated:: 1.2
`sparse` is deprecated in 1.2 and will be removed in 1.4. Use
`sparse_output` instead
sparse_output
bool
true
Will return sparse matrix if set True else will return an array
.. versionadded:: 1.2
`sparse` was renamed to `sparse_output`
openml-python
python
scikit-learn
sklearn
sklearn_1.2.1
openml-python
python
scikit-learn
sklearn
sklearn_1.2.1
variancethreshold
19549
37152
sklearn.feature_selection._variance_threshold.VarianceThreshold
sklearn.VarianceThreshold
sklearn.feature_selection._variance_threshold.VarianceThreshold
13
openml==0.14.1,sklearn==1.2.1
Feature selector that removes all low-variance features.
This feature selection algorithm looks only at the features (X), not the
desired outputs (y), and can thus be used for unsupervised learning.
2023-08-07T10:45:09
English
sklearn==1.2.1
numpy>=1.17.3
scipy>=1.3.2
joblib>=1.1.1
threadpoolctl>=2.0.0
threshold
float
0.0
Features with a training-set variance lower than this threshold will
be removed. The default is to keep all features with non-zero variance,
i.e. remove the features that have the same value in all samples.
openml-python
python
scikit-learn
sklearn
sklearn_1.2.1
randomforestclassifier
19550
37152
sklearn.ensemble._forest.RandomForestClassifier
sklearn.RandomForestClassifier
sklearn.ensemble._forest.RandomForestClassifier
33
openml==0.14.1,sklearn==1.2.1
A random forest classifier.
A random forest is a meta estimator that fits a number of decision tree
classifiers on various sub-samples of the dataset and uses averaging to
improve the predictive accuracy and control over-fitting.
The sub-sample size is controlled with the `max_samples` parameter if
`bootstrap=True` (default), otherwise the whole dataset is used to build
each tree.
2023-08-07T10:45:09
English
sklearn==1.2.1
numpy>=1.17.3
scipy>=1.3.2
joblib>=1.1.1
threadpoolctl>=2.0.0
bootstrap
bool
true
Whether bootstrap samples are used when building trees. If False, the
whole dataset is used to build each tree
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
null
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
"sqrt"
max_leaf_nodes
int
null
Grow trees 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
max_samples
int or float
null
If bootstrap is True, the number of samples to draw from X
to train each base estimator
- If None (default), then draw `X.shape[0]` samples
- If int, then draw `max_samples` samples
- If float, then draw `max_samples * X.shape[0]` samples. Thus,
`max_samples` should be in the interval `(0.0, 1.0]`
.. versionadded:: 0.22
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_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
max_features : {"sqrt", "log2", None}, int or float, default="sqrt"
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
`max(1, int(max_features * n_features_in_))` 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`
.. versionchanged:: 1.1
The default of `max_features` changed from `"auto"` to `"sqrt"`
.. deprecated:: 1.1
The `"auto"` option was deprecated in 1.1 and will be removed
in 1.3
Note: the search for a split does not stop until at lea...
n_estimators
int
100
The number of trees in the forest
.. versionchanged:: 0.22
The default value of ``n_estimators`` changed from 10 to 100
in 0.22
criterion : {"gini", "entropy", "log_loss"}, default="gini"
The function to measure the quality of a split. Supported criteria are
"gini" for the Gini impurity and "log_loss" and "entropy" both for the
Shannon information gain, see :ref:`tree_mathematical_formulation`
Note: This parameter is tree-specific
n_jobs
int
null
The number of jobs to run in parallel. :meth:`fit`, :meth:`predict`,
:meth:`decision_path` and :meth:`apply` are all parallelized over the
trees. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
context. ``-1`` means using all processors. See :term:`Glossary
<n_jobs>` for more details
oob_score
bool
false
Whether to use out-of-bag samples to estimate the generalization score
Only available if bootstrap=True
random_state
int
null
Controls both the randomness of the bootstrapping of the samples used
when building trees (if ``bootstrap=True``) and the sampling of the
features to consider when looking for the best split at each node
(if ``max_features < n_features``)
See :term:`Glossary <random_state>` for details
verbose
int
0
Controls the verbosity when fitting and predicting
warm_start
bool
false
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See :term:`Glossary <warm_start>` and
:ref:`gradient_boosting_warm_start` for details
class_weight : {"balanced", "balanced_subsample"}, dict or list of dicts, default=None
Weights associated with classes in the form ``{class_label: weight}``
If not given, 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 freq...
openml-python
python
scikit-learn
sklearn
sklearn_1.2.1
openml-python
python
scikit-learn
sklearn
sklearn_1.2.1