17433
1
sklearn.pipeline.Pipeline(columntransformer=sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)),variancethreshold=sklearn.feature_selection.variance_threshold.VarianceThreshold,decisiontreeclassifier=sklearn.tree.tree.DecisionTreeClassifier)
sklearn.Pipeline(ColumnTransformer,VarianceThreshold,DecisionTreeClassifier)
sklearn.pipeline.Pipeline
3
openml==0.10.2,sklearn==0.21.3
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``.
2019-11-22T02:17:21
English
sklearn==0.21.3
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": "columntransformer", "step_name": "columntransformer"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "variancethreshold", "step_name": "variancethreshold"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "decisiontreeclassifier", "step_name": "decisiontreeclassifier"}}]
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.
decisiontreeclassifier
17350
86
sklearn.tree.tree.DecisionTreeClassifier
sklearn.DecisionTreeClassifier
sklearn.tree.tree.DecisionTreeClassifier
58
openml==0.10.2,sklearn==0.21.3
A decision tree classifier.
2019-11-07T19:05:02
English
sklearn==0.21.3
numpy>=1.6.1
scipy>=0.9
class_weight
dict
null
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 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
string
"gini"
The function to measure the quality of a split. Supported criteria are
"gini" for the Gini impurity and "entropy" for the information gain
max_depth
int or None
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 or None
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_impurity_split
float
null
Threshold for early stopping in tree growth. A node will split
if its impurity is above the threshold, otherwise it is a leaf
.. deprecated:: 0.19
``min_impurity_split`` has been deprecated in favor of
``min_impurity_decrease`` in 0.19. The default value of
``min_impurity_split`` will change from 1e-7 to 0 in 0.23 and it
will be removed in 0.25. Use ``min_impurity_decrease`` instead
min_samples_leaf
int
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
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
presort
bool
false
Whether to presort the data to speed up the finding of best splits in
fitting. For the default settings of a decision tree on large
datasets, setting this to true may slow down the training process
When using either a smaller dataset or a restricted depth, this may
speed up the training.
random_state
int
null
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`
splitter
string
"best"
The strategy used to choose the split at each node. Supported
strategies are "best" to choose the best split and "random" to choose
the best random split
openml-python
python
scikit-learn
sklearn
sklearn_0.21.3
columntransformer
17421
1
sklearn.compose._column_transformer.ColumnTransformer(numeric=sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler),nominal=sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder))
sklearn.ColumnTransformer
sklearn.compose._column_transformer.ColumnTransformer
3
openml==0.10.2,sklearn==0.21.3
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.
2019-11-22T02:17:02
English
sklearn==0.21.3
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
"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": []}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "nominal", "step_name": "nominal", "argument_1": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]}}]
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.
numeric
17422
1
sklearn.pipeline.Pipeline(imputer=sklearn.preprocessing.imputation.Imputer,standardscaler=sklearn.preprocessing.data.StandardScaler)
sklearn.Pipeline(Imputer,StandardScaler)
sklearn.pipeline.Pipeline
8
openml==0.10.2,sklearn==0.21.3
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``.
2019-11-22T02:17:02
English
sklearn==0.21.3
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": "imputer", "step_name": "imputer"}}, {"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.
imputer
17423
1
sklearn.preprocessing.imputation.Imputer
sklearn.Imputer
sklearn.preprocessing.imputation.Imputer
50
openml==0.10.2,sklearn==0.21.3
Imputation transformer for completing missing values.
2019-11-22T02:17:02
English
sklearn==0.21.3
numpy>=1.6.1
scipy>=0.9
axis
integer
0
The axis along which to impute
- If `axis=0`, then impute along columns
- If `axis=1`, then impute along rows
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 sparse and `missing_values=0`;
- If `axis=0` and X is encoded as a CSR matrix;
- If `axis=1` and X is encoded as a CSC matrix.
missing_values
integer or
"NaN"
The placeholder for the missing values. All occurrences of
`missing_values` will be imputed. For missing values encoded as np.nan,
use the string value "NaN"
strategy
string
"mean"
The imputation strategy
- If "mean", then replace missing values using the mean along
the axis
- If "median", then replace missing values using the median along
the axis
- If "most_frequent", then replace missing using the most frequent
value along the axis
verbose
integer
0
Controls the verbosity of the imputer
openml-python
python
scikit-learn
sklearn
sklearn_0.21.3
standardscaler
17424
1
sklearn.preprocessing.data.StandardScaler
sklearn.StandardScaler
sklearn.preprocessing.data.StandardScaler
36
openml==0.10.2,sklearn==0.21.3
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...
2019-11-22T02:17:02
English
sklearn==0.21.3
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_0.21.3
openml-python
python
scikit-learn
sklearn
sklearn_0.21.3
nominal
17425
1
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder)
sklearn.Pipeline(SimpleImputer,OneHotEncoder)
sklearn.pipeline.Pipeline
4
openml==0.10.2,sklearn==0.21.3
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``.
2019-11-22T02:17:02
English
sklearn==0.21.3
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": "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.
simpleimputer
17377
10522
sklearn.impute._base.SimpleImputer
sklearn.SimpleImputer
sklearn.impute._base.SimpleImputer
10
openml==0.10.2,sklearn==0.21.3
Imputation transformer for completing missing values.
2019-11-18T12:23:12
English
sklearn==0.21.3
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
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
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_0.21.3
onehotencoder
17426
1
sklearn.preprocessing._encoders.OneHotEncoder
sklearn.OneHotEncoder
sklearn.preprocessing._encoders.OneHotEncoder
17
openml==0.10.2,sklearn==0.21.3
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.
2019-11-22T02:17:02
English
sklearn==0.21.3
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_0.21.3
openml-python
python
scikit-learn
sklearn
sklearn_0.21.3
openml-python
python
scikit-learn
sklearn
sklearn_0.21.3
variancethreshold
17427
1
sklearn.feature_selection.variance_threshold.VarianceThreshold
sklearn.VarianceThreshold
sklearn.feature_selection.variance_threshold.VarianceThreshold
28
openml==0.10.2,sklearn==0.21.3
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.
2019-11-22T02:17:02
English
sklearn==0.21.3
numpy>=1.6.1
scipy>=0.9
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_0.21.3
openml-python
python
scikit-learn
sklearn
sklearn_0.21.3