17691
8323
sklearn.pipeline.Pipeline(simpleimputer=sklearn.impute._base.SimpleImputer,onehotencoder=sklearn.preprocessing._encoders.OneHotEncoder,randomforestclassifier=sklearn.ensemble.forest.RandomForestClassifier)
sklearn.Pipeline(SimpleImputer,OneHotEncoder,RandomForestClassifier)
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``.
2020-05-17T14:27:10
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": "onehotencoder", "step_name": "onehotencoder"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "randomforestclassifier", "step_name": "randomforestclassifier"}}]
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
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
randomforestclassifier
17692
8323
sklearn.ensemble.forest.RandomForestClassifier
sklearn.RandomForestClassifier
sklearn.ensemble.forest.RandomForestClassifier
64
openml==0.10.2,sklearn==0.21.2
A random forest classifier.
A random forest is a meta estimator that fits a number of decision tree
classifiers on various sub-samples of the dataset and uses averaging to
improve the predictive accuracy and control over-fitting.
The sub-sample size is always the same as the original
input sample size but the samples are drawn with replacement if
`bootstrap=True` (default).
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sklearn==0.21.2
numpy>=1.6.1
scipy>=0.9
bootstrap
boolean
true
Whether bootstrap samples are used when building trees. If False, the
whole datset is used to build each tree
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))``
The "balanced_subsample" mode is the same as "balanced" except that
weights are computed based on the bootstrap sample for every tree
grown
For multi-output, the weights of each column of y will be multiplied
Note that these weights will be multiplied...
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
Note: this parameter is tree-specific
max_depth
integer 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
"auto"
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)` (same as "auto")
- 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 trees 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
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
n_estimators
integer
10
The number of trees in the forest
.. versionchanged:: 0.20
The default value of ``n_estimators`` will change from 10 in
version 0.20 to 100 in version 0.22
n_jobs
int or None
1
The number of jobs to run in parallel for both `fit` and `predict`
``None`` means 1 unless in a :obj:`joblib.parallel_backend` context
``-1`` means using all processors. See :term:`Glossary <n_jobs>`
for more details
oob_score
bool
false
Whether to use out-of-bag samples to estimate
the generalization accuracy
random_state
int
1
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`
verbose
int
0
Controls the verbosity when fitting and predicting
warm_start
bool
false
When set to ``True``, reuse the solution of the previous call to fit
and add more estimators to the ensemble, otherwise, just fit a whole
new forest. See :term:`the Glossary <warm_start>`
openml-python
python
scikit-learn
sklearn
sklearn_0.21.2
openml-python
python
scikit-learn
sklearn
sklearn_0.21.2