class sklearn.semi_supervised.LabelPropagation(kernel=’rbf’, gamma=20, n_neighbors=7, alpha=None, max_iter=1000, tol=0.001, n_jobs=None)
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Label Propagation classifier
Read more in the User Guide.
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See also
LabelSpreading
Xiaojin Zhu and Zoubin Ghahramani. Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University, 2002 http://pages.cs.wisc.edu/~jerryzhu/pub/CMU-CALD-02-107.pdf
>>> import numpy as np >>> from sklearn import datasets >>> from sklearn.semi_supervised import LabelPropagation >>> label_prop_model = LabelPropagation() >>> iris = datasets.load_iris() >>> rng = np.random.RandomState(42) >>> random_unlabeled_points = rng.rand(len(iris.target)) < 0.3 >>> labels = np.copy(iris.target) >>> labels[random_unlabeled_points] = -1 >>> label_prop_model.fit(iris.data, labels) ... LabelPropagation(...)
fit (X, y) | |
get_params ([deep]) | Get parameters for this estimator. |
predict (X) | Performs inductive inference across the model. |
predict_proba (X) | Predict probability for each possible outcome. |
score (X, y[, sample_weight]) | Returns the mean accuracy on the given test data and labels. |
set_params (**params) | Set the parameters of this estimator. |
__init__(kernel=’rbf’, gamma=20, n_neighbors=7, alpha=None, max_iter=1000, tol=0.001, n_jobs=None)
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get_params(deep=True)
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Get parameters for this estimator.
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predict(X)
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Performs inductive inference across the model.
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predict_proba(X)
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Predict probability for each possible outcome.
Compute the probability estimates for each single sample in X and each possible outcome seen during training (categorical distribution).
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score(X, y, sample_weight=None)
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Returns the mean accuracy on the given test data and labels.
In multi-label classification, this is the subset accuracy which is a harsh metric since you require for each sample that each label set be correctly predicted.
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set_params(**params)
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Set the parameters of this estimator.
The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form <component>__<parameter>
so that it’s possible to update each component of a nested object.
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© 2007–2018 The scikit-learn developers
Licensed under the 3-clause BSD License.
http://scikit-learn.org/stable/modules/generated/sklearn.semi_supervised.LabelPropagation.html