26046
1159
TEST60fa2bb50esklearn.pipeline.Pipeline(imputer=sklearn.impute._base.SimpleImputer,classifier=sklearn.dummy.DummyClassifier)
sklearn.Pipeline(SimpleImputer,DummyClassifier)
sklearn.pipeline.Pipeline
1
openml==0.14.1,sklearn==0.23.1
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``.
2024-01-15T10:51:12
English
sklearn==0.23.1
numpy>=1.13.3
scipy>=0.19.1
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
[{"oml-python:serialized_object": "component_reference", "value": {"key": "imputer", "step_name": "imputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "classifier", "step_name": "classifier"}}]
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.
imputer
26047
1159
TEST60fa2bb50esklearn.impute._base.SimpleImputer
sklearn.SimpleImputer
sklearn.impute._base.SimpleImputer
1
openml==0.14.1,sklearn==0.23.1
Imputation transformer for completing missing values.
2024-01-15T10:51:12
English
sklearn==0.23.1
numpy>=1.13.3
scipy>=0.19.1
joblib>=0.11
threadpoolctl>=2.0.0
add_indicator
boolean
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
boolean
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
string 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
number
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
string
"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 "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
integer
0
Controls the verbosity of the imputer
openml-python
python
scikit-learn
sklearn
sklearn_0.23.1
classifier
26048
1159
TEST60fa2bb50esklearn.dummy.DummyClassifier
sklearn.DummyClassifier
sklearn.dummy.DummyClassifier
1
openml==0.14.1,sklearn==0.23.1
DummyClassifier is a classifier that makes predictions using simple rules.
This classifier is useful as a simple baseline to compare with other
(real) classifiers. Do not use it for real problems.
2024-01-15T10:51:12
English
sklearn==0.23.1
numpy>=1.13.3
scipy>=0.19.1
joblib>=0.11
threadpoolctl>=2.0.0
constant
int or str or array
null
The explicit constant as predicted by the "constant" strategy. This
parameter is useful only for the "constant" strategy.
random_state
int
null
Controls the randomness to generate the predictions when
``strategy='stratified'`` or ``strategy='uniform'``
Pass an int for reproducible output across multiple function calls
See :term:`Glossary <random_state>`
strategy
str
"prior"
Strategy to use to generate predictions
* "stratified": generates predictions by respecting the training
set's class distribution
* "most_frequent": always predicts the most frequent label in the
training set
* "prior": always predicts the class that maximizes the class prior
(like "most_frequent") and ``predict_proba`` returns the class prior
* "uniform": generates predictions uniformly at random
* "constant": always predicts a constant label that is provided by
the user. This is useful for metrics that evaluate a non-majority
class
.. versionchanged:: 0.22
The default value of `strategy` will change to "prior" in version
0.24. Starting from version 0.22, a warning will be raised if
`strategy` is not explicitly set
.. versionadded:: 0.17
Dummy Classifier now supports prior fitting strategy using
parameter *prior*
openml-python
python
scikit-learn
sklearn
sklearn_0.23.1
openml-python
python
scikit-learn
sklearn
sklearn_0.23.1