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Highly interpretable classifiers for scikit learn, producing easily understood decision rules instead of black box models
Semi-supervised learning frameworks for python, which allow fitting scikit-learn classifiers to partially labeled data
Estimating and plotting the decision boundary (decision surface) of machine learning classifiers in higher dimensions (scikit-learn compatible)
Scikit-learn compatible implementations of the Random Rotation Ensemble idea of (Blaser & Fryzlewicz, 2016)
3D road line extraction, bird's-eye view projection, and junction detection for ROS stereo images
Simplified tree-based classifier and regressor for interpretable machine learning (scikit-learn compatible)
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