19162
32117
sklearn.pipeline.Pipeline(numerical=sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler),model=sklearn.tree._classes.DecisionTreeClassifier)
sklearn.Pipeline(Pipeline,DecisionTreeClassifier)
sklearn.pipeline.Pipeline
1
openml==0.12.2,sklearn==1.0.2
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`.
2022-09-25T22:44:07
English
sklearn==1.0.2
numpy>=1.14.6
scipy>=1.1.0
joblib>=0.11
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": "numerical", "step_name": "numerical"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "model", "step_name": "model"}}]
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.
model
19085
29930
sklearn.tree._classes.DecisionTreeClassifier
sklearn.DecisionTreeClassifier
sklearn.tree._classes.DecisionTreeClassifier
25
openml==0.12.2,sklearn==1.0.2
A decision tree classifier.
2022-03-08T11:40:33
English
sklearn==1.0.2
numpy>=1.14.6
scipy>=1.1.0
joblib>=0.11
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
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_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_1.0.2
numerical
19156
32117
sklearn.pipeline.Pipeline(Imputer=sklearn.impute._base.SimpleImputer,scaler=sklearn.preprocessing._data.StandardScaler)
sklearn.Pipeline(SimpleImputer,StandardScaler)
sklearn.pipeline.Pipeline
2
openml==0.12.2,sklearn==1.0.2
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`.
2022-09-25T00:28:40
English
sklearn==1.0.2
numpy>=1.14.6
scipy>=1.1.0
joblib>=0.11
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": "Imputer", "step_name": "Imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "scaler", "step_name": "scaler"}}]
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.
scaler
19075
29787
sklearn.preprocessing._data.StandardScaler
sklearn.StandardScaler
sklearn.preprocessing._data.StandardScaler
11
openml==0.12.2,sklearn==1.0.2
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 ...
2022-02-18T05:01:48
English
sklearn==1.0.2
numpy>=1.14.6
scipy>=1.1.0
joblib>=0.11
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_1.0.2
Imputer
19084
29930
sklearn.impute._base.SimpleImputer
sklearn.SimpleImputer
sklearn.impute._base.SimpleImputer
30
openml==0.12.2,sklearn==1.0.2
Imputation transformer for completing missing values.
2022-03-08T11:40:33
English
sklearn==1.0.2
numpy>=1.14.6
scipy>=1.1.0
joblib>=0.11
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`
should be set to `np.nan`, since `pd.NA` will be converted to `np.nan`
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
0
Controls the verbosity of the imputer
openml-python
python
scikit-learn
sklearn
sklearn_1.0.2
openml-python
python
scikit-learn
sklearn
sklearn_1.0.2
openml-python
python
scikit-learn
sklearn
sklearn_1.0.2