skactiveml.utils.is_labeled

skactiveml.utils.is_labeled(y, missing_label=nan)

Creates a boolean mask indicating present labels.

Parameters

yarray-like, shape (n_samples) or (n_samples, n_outputs)

Class labels to be checked w.r.t. to present labels.

missing_labelnumber | str | None | np.nan, optional (default=np.nan)

Symbol to represent a missing label.

Returns

is_unlabelednumpy.ndarray, shape (n_samples) or (n_samples, n_outputs)

Boolean mask indicating present labels in y.

Examples using skactiveml.utils.is_labeled

Greedy Sampling on the Feature Space (GSx)

Greedy Sampling on the Feature Space (GSx)

Greedy Sampling on the Target Space (GSy)

Greedy Sampling on the Target Space (GSy)

Improved Greedy Sampling (GSi)

Improved Greedy Sampling (GSi)

Expected Model Variance Reduction

Expected Model Variance Reduction

Expected Model Change

Expected Model Change

Regression Tree Based Active Learning (RT-AL) with Random Selection

Regression Tree Based Active Learning (RT-AL) with Random Selection

Regression Tree Based Active Learning (RT-AL) with Diversity Selection

Regression Tree Based Active Learning (RT-AL) with Diversity Selection

Regression Tree Based Active Learning (RT-AL) with Representativity Selection

Regression Tree Based Active Learning (RT-AL) with Representativity Selection

Query-by-Committee (QBC) with Empirical Variance

Query-by-Committee (QBC) with Empirical Variance

Expected Model Output Change

Expected Model Output Change

Regression based Kullback Leibler Divergence Maximization

Regression based Kullback Leibler Divergence Maximization

Dual strategy for Active Learning

Dual strategy for Active Learning