Binarize labels in a one-vs-all fashion.
Several regression and binary classification algorithms are available in scikit-learn. A simple way to extend these algorithms to the multi-class classification case is to use the so-called one-vs-all scheme.
At learning time, this simply consists in learning one regressor or binary classifier per class. In doing so, one needs to convert multi-class labels to binary labels (belong or does not belong to the class). LabelBinarizer makes this process easy with the transform method.
At prediction time, one assigns the class for which the corresponding model gave the greatest confidence. LabelBinarizer makes this easy with the inverse_transform method.
Read more in the User Guide.
Value with which negative labels must be encoded.
Value with which positive labels must be encoded.
True if the returned array from transform is desired to be in sparse CSR format.
Holds the label for each class.
Represents the type of the target data as evaluated by type_of_target. Possible type are ‘continuous’, ‘continuous-multioutput’, ‘binary’, ‘multiclass’, ‘multiclass-multioutput’, ‘multilabel-indicator’, and ‘unknown’.
True if the input data to transform is given as a sparse matrix,
False otherwise.
See also
label_binarize
Function to perform the transform operation of LabelBinarizer with fixed classes.
OneHotEncoder
Encode categorical features using a one-hot aka one-of-K scheme.
Examples
>>> from sklearn.preprocessing import LabelBinarizer
>>> lb = LabelBinarizer()
>>> lb.fit([1, 2, 6, 4, 2])
LabelBinarizer()
>>> lb.classes_
array([1, 2, 4, 6])
>>> lb.transform([1, 6])
array([[1, 0, 0, 0],
[0, 0, 0, 1]])
Binary targets transform to a column vector
>>> lb = LabelBinarizer()
>>> lb.fit_transform(['yes', 'no', 'no', 'yes'])
array([[1],
[0],
[0],
[1]])
Passing a 2D matrix for multilabel classification
>>> import numpy as np
>>> lb.fit(np.array([[0, 1, 1], [1, 0, 0]]))
LabelBinarizer()
>>> lb.classes_
array([0, 1, 2])
>>> lb.transform([0, 1, 2, 1])
array([[1, 0, 0],
[0, 1, 0],
[0, 0, 1],
[0, 1, 0]])
Fit label binarizer.
Target values. The 2-d matrix should only contain 0 and 1, represents multilabel classification.
Returns the instance itself.
Fit label binarizer/transform multi-class labels to binary labels.
The output of transform is sometimes referred to as the 1-of-K coding scheme.
Target values. The 2-d matrix should only contain 0 and 1, represents multilabel classification. Sparse matrix can be CSR, CSC, COO, DOK, or LIL.
Shape will be (n_samples, 1) for binary problems. Sparse matrix will be of CSR format.
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
A MetadataRequest encapsulating routing information.
Get parameters for this estimator.
If True, will return the parameters for this estimator and contained subobjects that are estimators.
Parameter names mapped to their values.
Transform binary labels back to multi-class labels.
Target values. All sparse matrices are converted to CSR before inverse transformation.
Threshold used in the binary and multi-label cases.
Use 0 when Y contains the output of decision_function (classifier). Use 0.5 when Y contains the output of predict_proba.
If None, the threshold is assumed to be half way between neg_label and pos_label.
Target values. Sparse matrix will be of CSR format.
Notes
In the case when the binary labels are fractional (probabilistic), inverse_transform chooses the class with the greatest value. Typically, this allows to use the output of a linear model’s decision_function method directly as the input of inverse_transform.
Configure whether metadata should be requested to be passed to the inverse_transform method.
Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with
enable_metadata_routing=True(seesklearn.set_config). Please check the User Guide on how the routing mechanism works.The options for each parameter are:
True: metadata is requested, and passed toinverse_transformif provided. The request is ignored if metadata is not provided.
False: metadata is not requested and the meta-estimator will not pass it toinverse_transform.
None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.
str: metadata should be passed to the meta-estimator with this given alias instead of the original name.The default (
sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.Added in version 1.3.
Metadata routing for threshold parameter in inverse_transform.
The updated object.
Set output container.
See Introducing the set_output API for an example on how to use the API.
Configure output of transform and fit_transform.
"default": Default output format of a transformer
"pandas": DataFrame output
"polars": Polars output
None: Transform configuration is unchanged
Added in version 1.4: "polars" option was added.
Estimator instance.
Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.
Estimator parameters.
Estimator instance.
Transform multi-class labels to binary labels.
The output of transform is sometimes referred to by some authors as the 1-of-K coding scheme.
Target values. The 2-d matrix should only contain 0 and 1, represents multilabel classification. Sparse matrix can be CSR, CSC, COO, DOK, or LIL.
Shape will be (n_samples, 1) for binary problems. Sparse matrix will be of CSR format.
RetroSearch is an open source project built by @garambo | Open a GitHub Issue
Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo
HTML:
3.2
| Encoding:
UTF-8
| Version:
0.7.4