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 (ALCE)

Active Learning with Cost Embedding (ALCE)

Discriminative Active Learning (DAL)

Discriminative Active Learning (DAL)

Dropout Query (DropQuery)

Dropout Query (DropQuery)

Epistemic Uncertainty Sampling (EpisUS)

Epistemic Uncertainty Sampling (EpisUS)

Fast Active Learning by Contrastive UNcertainty (FALCUN)

Fast Active Learning by Contrastive UNcertainty (FALCUN)

Batch Density-Diversity-Distribution-Distance Sampling (4DS)

Batch Density-Diversity-Distribution-Distance Sampling (4DS)

Density-Diversity-Distribution-Distance Sampling (4DS)

Density-Diversity-Distribution-Distance Sampling (4DS)

Bayesian Active Learning by Disagreement (BALD)

Bayesian Active Learning by Disagreement (BALD)

MaxHerding

MaxHerding

Monte-Carlo Expected Error Reduction (EER) with Log-Loss

Monte-Carlo Expected Error Reduction (EER) with Log-Loss

Monte-Carlo Expected Error Reduction (EER) with Misclassification-Loss

Monte-Carlo Expected Error Reduction (EER) with Misclassification-Loss

Parallel Utility Estimation Wrapper

Parallel Utility Estimation Wrapper

Probability Coverage (ProbCover)

Probability Coverage (ProbCover)

Multi-class Probabilistic Active Learning (McPAL)

Multi-class Probabilistic Active Learning (McPAL)

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 (QUIRE)

Querying Informative and Representative Examples (QUIRE)

Random Sampling

Random Sampling

Sub-sampling Wrapper

Sub-sampling Wrapper

Typical Clustering (TypiClust)

Typical Clustering (TypiClust)

Density-weighted Uncertainty Sampling (DWUS)

Density-weighted Uncertainty Sampling (DWUS)

Dual Strategy for Active Learning

Dual Strategy for Active Learning

Uncertainty Sampling (US) with Entropy

Uncertainty Sampling (US) with Entropy

Uncertainty Sampling (US) with Least-Confidence

Uncertainty Sampling (US) with Least-Confidence

Uncertainty Sampling (US) with Margin

Uncertainty Sampling (US) with Margin

Uncertainty Sampling with Expected Average Precision (USAP)

Uncertainty Sampling with Expected Average Precision (USAP)

Value of Information (VOI)

Value of Information (VOI)

Value of Information (VOI) on Labeled Samples

Value of Information (VOI) on Labeled Samples

Value of Information (VOI) on Unlabeled Samples

Value of Information (VOI) on Unlabeled Samples

Interval Estimation Threshold

Interval Estimation Threshold

Core Set + Greedy Selection

Core Set + Greedy Selection

Random Sampling

Random Sampling