skactiveml.utils.is_labeled

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

Creates a boolean mask indicating present labels.

Parameters

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

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

missing_labelnumber or str or None or np.nan, default=np.nan

Value to represent a missing label.

Returns

is_unlabelednp.ndarray of shape (n_samples,) or (n_samples, n_outputs)

Boolean mask indicating present labels in y.

Examples using skactiveml.utils.is_labeled

Expected Model Change Maximization

Expected Model Change Maximization

Expected Model Output Change

Expected Model Output Change

Expected Model Variance Reduction

Expected Model Variance Reduction

Greedy Sampling on the Target Space (GSy)

Greedy Sampling on the Target Space (GSy)

Improved Greedy Sampling (GSi)

Improved Greedy Sampling (GSi)

Greedy Sampling on the Feature Space (GSx)

Greedy Sampling on the Feature Space (GSx)

Regression based Kullback Leibler Divergence Maximization

Regression based Kullback Leibler Divergence Maximization

Query-by-Committee (QBC) with Empirical Variance

Query-by-Committee (QBC) with Empirical Variance

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 Random Selection

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

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

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

Dual Strategy for Active Learning

Dual Strategy for Active Learning