18607
8323
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,columntransformer=sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),logisticregression=sklearn.linear_model.logistic.LogisticRegression)
sklearn.Pipeline(SimpleImputer,ColumnTransformer,LogisticRegression)
sklearn.pipeline.Pipeline
2
openml==0.10.2,sklearn==0.21.2
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``.
2020-08-07T19:21:48
English
sklearn==0.21.2
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": "simpleimputer", "step_name": "simpleimputer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "logisticregression", "step_name": "logisticregression"}}]
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
boolean
false
If True, the time elapsed while fitting each step will be printed as it
is completed.
simpleimputer
17407
1
sklearn.impute._base.SimpleImputer
sklearn.SimpleImputer
sklearn.impute._base.SimpleImputer
11
openml==0.10.2,sklearn==0.21.2
Imputation transformer for completing missing values.
2019-11-22T01:19:36
English
sklearn==0.21.2
numpy>=1.6.1
scipy>=0.9
add_indicator
boolean
false
If True, a `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
-1
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
strategy
string
"constant"
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_0.21.2
logisticregression
17462
10792
sklearn.linear_model.logistic.LogisticRegression
sklearn.LogisticRegression
sklearn.linear_model.logistic.LogisticRegression
33
openml==0.10.2,sklearn==0.21.2
Logistic Regression (aka logit, MaxEnt) classifier.
In the multiclass case, the training algorithm uses the one-vs-rest (OvR)
scheme if the 'multi_class' option is set to 'ovr', and uses the
cross-entropy loss if the 'multi_class' option is set to 'multinomial'.
(Currently the 'multinomial' option is supported only by the 'lbfgs',
'sag', 'saga' and 'newton-cg' solvers.)
This class implements regularized logistic regression using the
'liblinear' library, 'newton-cg', 'sag', 'saga' and 'lbfgs' solvers. **Note
that regularization is applied by default**. It can handle both dense
and sparse input. Use C-ordered arrays or CSR matrices containing 64-bit
floats for optimal performance; any other input format will be converted
(and copied).
The 'newton-cg', 'sag', and 'lbfgs' solvers support only L2 regularization
with primal formulation, or no regularization. The 'liblinear' solver
supports both L1 and L2 regularization, with a dual formulation only for
the L2 penalty. The Elastic-Net regularization is only su...
2019-12-16T00:38:14
English
sklearn==0.21.2
numpy>=1.6.1
scipy>=0.9
C
float
100000000
Inverse of regularization strength; must be a positive float
Like in support vector machines, smaller values specify stronger
regularization
class_weight
dict or
null
Weights associated with classes in the form ``{class_label: weight}``
If not given, all classes are supposed to have weight one
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))``
Note that these weights will be multiplied with sample_weight (passed
through the fit method) if sample_weight is specified
.. versionadded:: 0.17
*class_weight='balanced'*
dual
bool
false
Dual or primal formulation. Dual formulation is only implemented for
l2 penalty with liblinear solver. Prefer dual=False when
n_samples > n_features
fit_intercept
bool
true
Specifies if a constant (a.k.a. bias or intercept) should be
added to the decision function
intercept_scaling
float
1
Useful only when the solver 'liblinear' is used
and self.fit_intercept is set to True. In this case, x becomes
[x, self.intercept_scaling],
i.e. a "synthetic" feature with constant value equal to
intercept_scaling is appended to the instance vector
The intercept becomes ``intercept_scaling * synthetic_feature_weight``
Note! the synthetic feature weight is subject to l1/l2 regularization
as all other features
To lessen the effect of regularization on synthetic feature weight
(and therefore on the intercept) intercept_scaling has to be increased
l1_ratio
float or None
null
The Elastic-Net mixing parameter, with ``0 <= l1_ratio <= 1``. Only
used if ``penalty='elasticnet'`. Setting ``l1_ratio=0`` is equivalent
to using ``penalty='l2'``, while setting ``l1_ratio=1`` is equivalent
to using ``penalty='l1'``. For ``0 < l1_ratio <1``, the penalty is a
combination of L1 and L2.
max_iter
int
100
Maximum number of iterations taken for the solvers to converge
multi_class
str
"warn"
If the option chosen is 'ovr', then a binary problem is fit for each
label. For 'multinomial' the loss minimised is the multinomial loss fit
across the entire probability distribution, *even when the data is
binary*. 'multinomial' is unavailable when solver='liblinear'
'auto' selects 'ovr' if the data is binary, or if solver='liblinear',
and otherwise selects 'multinomial'
.. versionadded:: 0.18
Stochastic Average Gradient descent solver for 'multinomial' case
.. versionchanged:: 0.20
Default will change from 'ovr' to 'auto' in 0.22
n_jobs
int or None
null
Number of CPU cores used when parallelizing over classes if
multi_class='ovr'". This parameter is ignored when the ``solver`` is
set to 'liblinear' regardless of whether 'multi_class' is specified or
not. ``None`` means 1 unless in a :obj:`joblib.parallel_backend`
context. ``-1`` means using all processors
See :term:`Glossary <n_jobs>` for more details
penalty
str
"l2"
Used to specify the norm used in the penalization. The 'newton-cg',
'sag' and 'lbfgs' solvers support only l2 penalties. 'elasticnet' is
only supported by the 'saga' solver. If 'none' (not supported by the
liblinear solver), no regularization is applied
.. versionadded:: 0.19
l1 penalty with SAGA solver (allowing 'multinomial' + L1)
random_state
int
22823
The seed of the pseudo random number generator to use when shuffling
the data. 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`. Used when ``solver`` == 'sag' or
'liblinear'
solver
str
"warn"
Algorithm to use in the optimization problem
- For small datasets, 'liblinear' is a good choice, whereas 'sag' and
'saga' are faster for large ones
- For multiclass problems, only 'newton-cg', 'sag', 'saga' and 'lbfgs'
handle multinomial loss; 'liblinear' is limited to one-versus-rest
schemes
- 'newton-cg', 'lbfgs', 'sag' and 'saga' handle L2 or no penalty
- 'liblinear' and 'saga' also handle L1 penalty
- 'saga' also supports 'elasticnet' penalty
- 'liblinear' does not handle no penalty
Note that 'sag' and 'saga' fast convergence is only guaranteed on
features with approximately the same scale. You can
preprocess the data with a scaler from sklearn.preprocessing
.. versionadded:: 0.17
Stochastic Average Gradient descent solver
.. versionadded:: 0.19
SAGA solver
.. versionchanged:: 0.20
Default will change from 'liblinear' to 'lbfgs' in 0.22
tol
float
0.0001
Tolerance for stopping criteria
verbose
int
0
For the liblinear and lbfgs solvers set verbose to any positive
number for verbosity
warm_start
bool
false
When set to True, reuse the solution of the previous call to fit as
initialization, otherwise, just erase the previous solution
Useless for liblinear solver. See :term:`the Glossary <warm_start>`
.. versionadded:: 0.17
*warm_start* to support *lbfgs*, *newton-cg*, *sag*, *saga* solvers
openml-python
python
scikit-learn
sklearn
sklearn_0.21.2
columntransformer
18299
8323
sklearn.compose._column_transformer.ColumnTransformer(num=sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler),cat=sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))
sklearn.ColumnTransformer
sklearn.compose._column_transformer.ColumnTransformer
6
openml==0.10.2,sklearn==0.21.2
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.
2020-05-19T22:11:21
English
sklearn==0.21.2
numpy>=1.6.1
scipy>=0.9
n_jobs
int or None
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": "num", "step_name": "num", "argument_1": [true, true, false, true, false, false, true, true, true, true, true, true, true, true, true, true, true, true, true, false]}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "cat", "step_name": "cat", "argument_1": [false, false, true, false, true, true, false, false, false, false, false, false, false, false, false, false, false, false, false, true]}}]
List of (name, transformer, column(s)) tuples specifying the
transformer objects to be applied to subsets of the data
verbose
boolean
false
If True, the time elapsed while fitting each transformer will be
printed as it is completed.
num
18300
8323
sklearn.pipeline.Pipeline(standardscaler=sklearn.preprocessing.data.StandardScaler)
sklearn.Pipeline(StandardScaler)
sklearn.pipeline.Pipeline
6
openml==0.10.2,sklearn==0.21.2
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``.
2020-05-19T22:11:21
English
sklearn==0.21.2
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": "standardscaler", "step_name": "standardscaler"}}]
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
boolean
false
If True, the time elapsed while fitting each step will be printed as it
is completed.
standardscaler
17405
1
sklearn.preprocessing.data.StandardScaler
sklearn.StandardScaler
sklearn.preprocessing.data.StandardScaler
35
openml==0.10.2,sklearn==0.21.2
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 the
`transform` method.
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 aroun...
2019-11-22T01:19:36
English
sklearn==0.21.2
numpy>=1.6.1
scipy>=0.9
copy
boolean
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
boolean
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
boolean
true
If True, scale the data to unit variance (or equivalently,
unit standard deviation).
openml-python
python
scikit-learn
sklearn
sklearn_0.21.2
openml-python
python
scikit-learn
sklearn
sklearn_0.21.2
cat
18301
8323
sklearn.pipeline.Pipeline(onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)
sklearn.Pipeline(OneHotEncoder)
sklearn.pipeline.Pipeline
7
openml==0.10.2,sklearn==0.21.2
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``.
2020-05-19T22:11:21
English
sklearn==0.21.2
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": "onehotencoder", "step_name": "onehotencoder"}}]
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
boolean
false
If True, the time elapsed while fitting each step will be printed as it
is completed.
onehotencoder
17408
1
sklearn.preprocessing._encoders.OneHotEncoder
sklearn.OneHotEncoder
sklearn.preprocessing._encoders.OneHotEncoder
16
openml==0.10.2,sklearn==0.21.2
Encode categorical integer 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.
By default, the encoder derives the categories based on the unique values
in each feature. Alternatively, you can also specify the `categories`
manually.
The OneHotEncoder previously assumed that the input features take on
values in the range [0, max(values)). This behaviour is deprecated.
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.
2019-11-22T01:19:36
English
sklearn==0.21.2
numpy>=1.6.1
scipy>=0.9
categorical_features
'all' or array of indices or mask
null
Specify what features are treated as categorical
- 'all': All features are treated as categorical
- array of indices: Array of categorical feature indices
- mask: Array of length n_features and with dtype=bool
Non-categorical features are always stacked to the right of the matrix
.. deprecated:: 0.20
The `categorical_features` keyword was deprecated in version
0.20 and will be removed in 0.22
You can use the ``ColumnTransformer`` instead.
categories
'auto' or a list of lists
null
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
drop
'first' or a list
null
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 a neural network or an unregularized regression
dtype
number type
{"oml-python:serialized_object": "type", "value": "np.float64"}
Desired dtype of output
handle_unknown
'error' or
"ignore"
Whether to raise an error or ignore if an unknown categorical feature
is present during transform (default is to raise). When this parameter
is set to 'ignore' and 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
n_values
'auto'
null
Number of values per feature
- 'auto' : determine value range from training data
sparse
boolean
true
Will return sparse matrix if set True else will return an array
openml-python
python
scikit-learn
sklearn
sklearn_0.21.2
openml-python
python
scikit-learn
sklearn
sklearn_0.21.2
openml-python
python
scikit-learn
sklearn
sklearn_0.21.2
openml-python
python
scikit-learn
sklearn
sklearn_0.21.2