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# `sklearn.semi_supervised`.SelfTrainingClassifier

class sklearn.semi_supervised.SelfTrainingClassifier(base_estimator, threshold=0.75, criterion='threshold', k_best=10, max_iter=10, verbose=False)

  Self-training classifier.

  This metaestimator allows a given supervised classifier to function as a semi-supervised classifier, allowing it to learn from unlabeled data. It does this by iteratively predicting pseudo-labels for the unlabeled data and adding them to the training set.

  The classifier will continue iterating until either max_iter is reached, or no pseudo-labels were added to the training set in the previous iteration.

  Read more in the User Guide.

  Parameters:
    base_estimator estimator object

      An estimator object implementing `fit` and `predict_proba`. Invoking the `fit` method will fit a clone of the passed estimator, which will be stored in the `base_estimator_` attribute.

    threshold float, default=0.75

      The decision threshold for use with `criterion='threshold'`. Should be in [0, 1). When using the `'threshold'` criterion, a well calibrated classifier should be used.

    criterion{‘threshold’, ‘k_best’}, default=’threshold’

      The selection criterion used to select which labels to add to the training set. If `'threshold'`, pseudo-labels with prediction probabilities above `threshold` are added to the dataset. If `'k_best'`, the `k_best` pseudo-labels with highest prediction probabilities are added to the dataset. When using the ‘threshold’ criterion, a well calibrated classifier should be used.

    k_best int, default=10

      The amount of samples to add in each iteration. Only used when `criterion='k_best'`.

    max_iter int or None, default=10

      Maximum number of iterations allowed. Should be greater than or equal to 0. If it is `None`, the classifier will continue to predict labels until no new pseudo-labels are added, or all unlabeled samples have been labeled.

    verbose bool, default=False

      Enable verbose output.

  Attributes:
    base_estimator_estimator object

      The fitted estimator.

    classes_ndarray or list of ndarray of shape (n_classes,)

      Class labels for each output. (Taken from the trained `base_estimator_`).

    transduction_ndarray of shape (n_samples,)

      The labels used for the final fit of the classifier, including pseudo-labels added during fit.

    labeled_iter_ndarray of shape (n_samples,)

      The iteration in which each sample was labeled. When a sample has iteration 0, the sample was already labeled in the original dataset. When a sample has iteration -1, the sample was not labeled in any iteration.

    n_features_in_int

      Number of features seen during fit.

      New in version 0.24.

    feature_names_in_ndarray of shape (`n_features_in_`,)

      Names of features seen during fit. Defined only when `X` has feature names that are all strings.

      New in version 1.0.

    n_iter_int

      The number of rounds of self-training, that is the number of times the base estimator is fitted on relabeled variants of the training set.

    termination_condition_{‘max_iter’, ‘no_change’, ‘all_labeled’}

      The reason that fitting was stopped.

      - `'max_iter'`: `n_iter_` reached `max_iter`.

      - `'no_change'`: no new labels were predicted.

      - `'all_labeled'`: all unlabeled samples were labeled before `max_iter` was reached.

  See also

  `LabelPropagation`

    Label propagation classifier.

  `LabelSpreading`

    Label spreading model for semi-supervised learning.

  References

  David Yarowsky. 1995. Unsupervised word sense disambiguation rivaling supervised methods. In Proceedings of the 33rd annual meeting on Association for Computational Linguistics (ACL ‘95). Association for Computational Linguistics, Stroudsburg, PA, USA, 189-196.

  Examples

      >>> import numpy as np
      >>> from sklearn import datasets
      >>> from sklearn.semi_supervised import SelfTrainingClassifier
      >>> from sklearn.svm import SVC
      >>> rng = np.random.RandomState(42)
      >>> iris = datasets.load_iris()
      >>> random_unlabeled_points = rng.rand(iris.target.shape[0]) < 0.3
      >>> iris.target[random_unlabeled_points] = -1
      >>> svc = SVC(probability=True, gamma="auto")
      >>> self_training_model = SelfTrainingClassifier(svc)
      >>> self_training_model.fit(iris.data, iris.target)
      SelfTrainingClassifier(...)

  Methods

  `decision_function`(X) | Call decision function of the `base_estimator`.
  `fit`(X, y) | Fit self-training classifier using `X`, `y` as training data.
  `get_metadata_routing`() | Get metadata routing of this object.
  `get_params`([deep]) | Get parameters for this estimator.
  `predict`(X) | Predict the classes of `X`.
  `predict_log_proba`(X) | Predict log probability for each possible outcome.
  `predict_proba`(X) | Predict probability for each possible outcome.
  `score`(X, y) | Call score on the `base_estimator`.
  `set_params`(**params) | Set the parameters of this estimator.

  decision_function(X)

    Call decision function of the `base_estimator`.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Array representing the data.

    Returns:
      y ndarray of shape (n_samples, n_features)

        Result of the decision function of the `base_estimator`.

  fit(X, y)

    Fit self-training classifier using `X`, `y` as training data.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Array representing the data.

      y{array-like, sparse matrix} of shape (n_samples,)

        Array representing the labels. Unlabeled samples should have the label -1.

    Returns:
      self object

        Fitted estimator.

  get_metadata_routing()

    Get metadata routing of this object.

    Please check User Guide on how the routing mechanism works.

    Returns:
      routing MetadataRequest

        A `MetadataRequest` encapsulating routing information.

  get_params(deep=True)

    Get parameters for this estimator.

    Parameters:
      deep bool, default=True

        If True, will return the parameters for this estimator and contained subobjects that are estimators.

    Returns:
      params dict

        Parameter names mapped to their values.

  predict(X)

    Predict the classes of `X`.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Array representing the data.

    Returns:
      y ndarray of shape (n_samples,)

        Array with predicted labels.

  predict_log_proba(X)

    Predict log probability for each possible outcome.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Array representing the data.

    Returns:
      y ndarray of shape (n_samples, n_features)

        Array with log prediction probabilities.

  predict_proba(X)

    Predict probability for each possible outcome.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Array representing the data.

    Returns:
      y ndarray of shape (n_samples, n_features)

        Array with prediction probabilities.

  score(X, y)

    Call score on the `base_estimator`.

    Parameters:
      X{array-like, sparse matrix} of shape (n_samples, n_features)

        Array representing the data.

      y array-like of shape (n_samples,)

        Array representing the labels.

    Returns:
      score float

        Result of calling score on the `base_estimator`.

  set_params(**params)

    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.

    Parameters:
      **params dict

        Estimator parameters.

    Returns:
      self estimator instance

        Estimator instance.

## Examples using `sklearn.semi_supervised.SelfTrainingClassifier`

Release Highlights for scikit-learn 0.24

Release Highlights for scikit-learn 0.24

Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset

Decision boundary of semi-supervised classifiers versus SVM on the Iris dataset

Effect of varying threshold for self-training

Effect of varying threshold for self-training

Semi-supervised Classification on a Text Dataset

Semi-supervised Classification on a Text Dataset