skactiveml.visualization.plot_decision_boundary

skactiveml.visualization.plot_decision_boundary(clf, feature_bound, ax=None, res=21, boundary_dict=None, confidence=0.75, cmap='coolwarm', confidence_dict=None)

Plot the decision boundary of the given classifier.

Parameters

clfsklearn.base.ClassifierMixin

The fitted classifier whose decision boundary is plotted. If confidence is not None, the classifier must implement the predict_proba function.

feature_boundarray-like of shape [[xmin, ymin], [xmax, ymax]]

Determines the area in which the boundary is plotted.

axmatplotlib.axes.Axes or List, default=None

The axis on which the decision boundary is plotted. If ax is a List, each entry has to be an matplotlib.axes.Axes.

resint, default=21

The resolution of the plot.

boundary_dictdict, default=None

Additional parameters for the boundary contour.

confidencescalar or None, default=0.75

The confidence interval plotted with dashed lines. It is not plotted if confidence is None. Must be in the open interval (0.5, 1). The value stands for the ratio best class / second best class.

cmapstr or matplotlib.colors.Colormap, default=’coolwarm_r’

The colormap for the confidence levels.

confidence_dictdict, default=None

Additional parameters for the confidence contour. Must not contain a colormap because cmap is used.

Returns

axmatplotlib.axes.Axes or List

The axis on which the boundary was plotted or the list of axis if ax was a list.

Examples using skactiveml.visualization.plot_decision_boundary

Batch Active Learning by Diverse Gradient Embedding (BADGE)

Batch Active Learning by Diverse Gradient Embedding (BADGE)

Batch Bayesian Active Learning by Disagreement (BatchBALD)

Batch Bayesian Active Learning by Disagreement (BatchBALD)

Clustering Uncertainty-weighted Embeddings (CLUE)

Clustering Uncertainty-weighted Embeddings (CLUE)

Contrastive Active Learning (CAL)

Contrastive Active Learning (CAL)

Core Set

Core Set

Active Learning with Cost Embedding

Active Learning with Cost Embedding

Discriminative Active Learning

Discriminative Active Learning

Dropout Query (DropQuery)

Dropout Query (DropQuery)

Epistemic Uncertainty Sampling

Epistemic Uncertainty Sampling

Fast Active Learning by Contrastive UNcertainty (FALCUN)

Fast Active Learning by Contrastive UNcertainty (FALCUN)

Batch Density-Diversity-Distribution-Distance Sampling

Batch Density-Diversity-Distribution-Distance Sampling

Density-Diversity-Distribution-Distance Sampling

Density-Diversity-Distribution-Distance Sampling

Bayesian Active Learning by Disagreement (BALD)

Bayesian Active Learning by Disagreement (BALD)

Monte-Carlo EER with Log-Loss

Monte-Carlo EER with Log-Loss

Monte-Carlo EER with Misclassification-Loss

Monte-Carlo EER with Misclassification-Loss

Parallel Utility Estimation Wrapper

Parallel Utility Estimation Wrapper

Probability Coverage (ProbCover)

Probability Coverage (ProbCover)

Multi-class Probabilistic Active Learning

Multi-class Probabilistic Active Learning

Query-by-Committee (QBC) with Kullback-Leibler Divergence

Query-by-Committee (QBC) with Kullback-Leibler Divergence

Query-by-Committee (QBC) with Variation Ratios

Query-by-Committee (QBC) with Variation Ratios

Query-by-Committee (QBC) with Vote Entropy

Query-by-Committee (QBC) with Vote Entropy

Querying Informative and Representative Examples

Querying Informative and Representative Examples

Random Sampling

Random Sampling

Sub-sampling Wrapper

Sub-sampling Wrapper

Typical Clustering (TypiClust)

Typical Clustering (TypiClust)

Density-weighted Uncertainty Sampling

Density-weighted Uncertainty Sampling

Dual Strategy for Active Learning

Dual Strategy for Active Learning

Expected Average Precision

Expected Average Precision

Uncertainty Sampling with Entropy

Uncertainty Sampling with Entropy

Uncertainty Sampling with Least-Confidence

Uncertainty Sampling with Least-Confidence

Uncertainty Sampling with Margin

Uncertainty Sampling with Margin

Value of Information

Value of Information

Value of Information on Labeled Samples

Value of Information on Labeled Samples

Value of Information on Unlabeled Samples

Value of Information on Unlabeled Samples