19444
10554
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder),decisiontreeclassifier=sklearn.tree._classes.DecisionTreeClassifier)
sklearn.Pipeline(ColumnTransformer,DecisionTreeClassifier)
sklearn.pipeline.Pipeline
6
openml==0.13.1,sklearn==1.1.2
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==1.1.2
numpy>=1.17.3
scipy>=1.3.2
joblib>=1.0.0
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": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
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
19445
10554
sklearn.compose._column_transformer.ColumnTransformer(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)
sklearn.ColumnTransformer
sklearn.compose._column_transformer.ColumnTransformer
5
openml==0.13.1,sklearn==1.1.2
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.
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English
sklearn==1.1.2
numpy>=1.17.3
scipy>=1.3.2
joblib>=1.0.0
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
"drop"
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": "simpleimputer", "step_name": "simpleimputer", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cont"}}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "onehotencoder", "step_name": "onehotencoder", "argument_1": {"oml-python:serialized_object": "function", "value": "openml.extensions.sklearn.cat"}}}]
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
simpleimputer
19446
10554
sklearn.impute._base.SimpleImputer
sklearn.SimpleImputer
sklearn.impute._base.SimpleImputer
47
openml==0.13.1,sklearn==1.1.2
Univariate imputer for completing missing values with simple strategies.
Replace missing values using a descriptive statistic (e.g. mean, median, or
most frequent) along each column, or using a constant value.
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English
sklearn==1.1.2
numpy>=1.17.3
scipy>=1.3.2
joblib>=1.0.0
threadpoolctl>=2.0.0
add_indicator
bool
false
If True, a :class:`MissingIndicator` transform will stack onto output
of the imputer's transform. This allows a predictive estimator
to account for missingness despite imputation. If a feature has no
missing values at fit/train time, the feature won't appear on
the missing indicator even if there are missing values at
transform/test time.
copy
bool
true
If True, a copy of X will be created. If False, imputation will
be done in-place whenever possible. Note that, in the following cases,
a new copy will always be made, even if `copy=False`:
- If `X` is not an array of floating values;
- If `X` is encoded as a CSR matrix;
- If `add_indicator=True`
fill_value
str or numerical value
null
When strategy == "constant", fill_value is used to replace all
occurrences of missing_values
If left to the default, fill_value will be 0 when imputing numerical
data and "missing_value" for strings or object data types
missing_values
int
NaN
The placeholder for the missing values. All occurrences of
`missing_values` will be imputed. For pandas' dataframes with
nullable integer dtypes with missing values, `missing_values`
can be set to either `np.nan` or `pd.NA`
strategy
str
"mean"
The imputation strategy
- If "mean", then replace missing values using the mean along
each column. Can only be used with numeric data
- If "median", then replace missing values using the median along
each column. Can only be used with numeric data
- If "most_frequent", then replace missing using the most frequent
value along each column. Can be used with strings or numeric data
If there is more than one such value, only the smallest is returned
- If "constant", then replace missing values with fill_value. Can be
used with strings or numeric data
.. versionadded:: 0.20
strategy="constant" for fixed value imputation
verbose
int
"deprecated"
Controls the verbosity of the imputer
.. deprecated:: 1.1
The 'verbose' parameter was deprecated in version 1.1 and will be
removed in 1.3. A warning will always be raised upon the removal of
empty columns in the future version
openml-python
python
scikit-learn
sklearn
sklearn_1.1.2
onehotencoder
19447
10554
sklearn.preprocessing._encoders.OneHotEncoder
sklearn.OneHotEncoder
sklearn.preprocessing._encoders.OneHotEncoder
41
openml==0.13.1,sklearn==1.1.2
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``
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==1.1.2
numpy>=1.17.3
scipy>=1.3.2
joblib>=1.0.0
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
true
Will return sparse matrix if set True else will return an array
openml-python
python
scikit-learn
sklearn
sklearn_1.1.2
openml-python
python
scikit-learn
sklearn
sklearn_1.1.2
decisiontreeclassifier
19448
10554
sklearn.tree._classes.DecisionTreeClassifier
sklearn.DecisionTreeClassifier
sklearn.tree._classes.DecisionTreeClassifier
38
openml==0.13.1,sklearn==1.1.2
A decision tree classifier.
2023-05-16T15:38:38
English
sklearn==1.1.2
numpy>=1.17.3
scipy>=1.3.2
joblib>=1.0.0
threadpoolctl>=2.0.0
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
1
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
`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`
.. 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 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_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
random_state
int
null
Controls the randomness of the estimator. The features are always
randomly permuted at each split, even if ``splitter`` is set to
``"best"``. When ``max_features < n_features``, the algorithm will
select ``max_features`` at random at each split before finding the best
split among them. But the best found split may vary across different
runs, even if ``max_features=n_features``. That is the case, if the
improvement of the criterion is identical for several splits and one
split has to be selected at random. To obtain a deterministic behaviour
during fitting, ``random_state`` has to be fixed to an integer
See :term:`Glossary <random_state>` for details
splitter
"best"
openml-python
python
scikit-learn
sklearn
sklearn_1.1.2
openml-python
python
scikit-learn
sklearn
sklearn_1.1.2