18575
12269
sklearn.pipeline.Pipeline(step_0=sklearn.preprocessing._data.QuantileTransformer,step_1=sklearn.naive_bayes.MultinomialNB)
sklearn.Pipeline(QuantileTransformer,MultinomialNB)
sklearn.pipeline.Pipeline
1
openml==0.10.2,sklearn==0.22.1
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``.
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English
sklearn==0.22.1
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": "step_0", "step_name": "step_0"}}, {"oml-python:serialized_object": "component_reference", "value": {"key": "step_1", "step_name": "step_1"}}]
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.
step_1
17700
12269
sklearn.naive_bayes.MultinomialNB
sklearn.MultinomialNB
sklearn.naive_bayes.MultinomialNB
6
openml==0.10.2,sklearn==0.22.1
Naive Bayes classifier for multinomial models
The multinomial Naive Bayes classifier is suitable for classification with
discrete features (e.g., word counts for text classification). The
multinomial distribution normally requires integer feature counts. However,
in practice, fractional counts such as tf-idf may also work.
2020-05-18T19:37:58
English
sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
alpha
float
0.8683682482864065
Additive (Laplace/Lidstone) smoothing parameter
(0 for no smoothing)
class_prior
array
null
Prior probabilities of the classes. If specified the priors are not
adjusted according to the data.
fit_prior
boolean
false
Whether to learn class prior probabilities or not
If false, a uniform prior will be used
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
step_0
17755
12269
sklearn.preprocessing._data.QuantileTransformer
sklearn.QuantileTransformer
sklearn.preprocessing._data.QuantileTransformer
1
openml==0.10.2,sklearn==0.22.1
Transform features using quantiles information.
This method transforms the features to follow a uniform or a normal
distribution. Therefore, for a given feature, this transformation tends
to spread out the most frequent values. It also reduces the impact of
(marginal) outliers: this is therefore a robust preprocessing scheme.
The transformation is applied on each feature independently. First an
estimate of the cumulative distribution function of a feature is
used to map the original values to a uniform distribution. The obtained
values are then mapped to the desired output distribution using the
associated quantile function. Features values of new/unseen data that fall
below or above the fitted range will be mapped to the bounds of the output
distribution. Note that this transform is non-linear. It may distort linear
correlations between variables measured at the same scale but renders
variables measured at different scales more directly comparable.
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sklearn==0.22.1
numpy>=1.6.1
scipy>=0.9
copy
boolean
false
Set to False to perform inplace transformation and avoid a copy (if the
input is already a numpy array).
ignore_implicit_zeros
bool
false
Only applies to sparse matrices. If True, the sparse entries of the
matrix are discarded to compute the quantile statistics. If False,
these entries are treated as zeros
n_quantiles
int
1200
Number of quantiles to be computed. It corresponds to the number
of landmarks used to discretize the cumulative distribution function
If n_quantiles is larger than the number of samples, n_quantiles is set
to the number of samples as a larger number of quantiles does not give
a better approximation of the cumulative distribution function
estimator
output_distribution
str
"normal"
Marginal distribution for the transformed data. The choices are
'uniform' (default) or 'normal'
random_state
int
42
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. Note that this is used by subsampling and smoothing
noise
subsample
int
34199164
Maximum number of samples used to estimate the quantiles for
computational efficiency. Note that the subsampling procedure may
differ for value-identical sparse and dense matrices
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1
openml-python
python
scikit-learn
sklearn
sklearn_0.22.1