![]() Classifier decision boundaries for any scikit-learn model.ipynb ( colab). ![]() TensorFlow-based examples ( colab) Also see blog at Visualizing TensorFlow Decision Forest Trees with dtreeviz.See Installation instructions then take a look at the specific notebooks for the supported ML library you're using: If you look in notebook classifier-boundary-animations.ipynb, you will see code that generates animations such as the following (animated png files): Sometimes it's helpful to see animations that change some of the hyper parameters. (As it does not work with trees specifically, the function does not use adaptors obtained from dtreeviz.model().) See classifier-decision-boundaries.ipynb. That means any model from scikit-learn should work (but we also made it work with Keras models that define predict()). This method is not limited to tree models, by the way, and should work with any model that answers method predict_proba(). With major code and visualization clean up contributions done by Matthew Epland Sample Visualizations Tree visualizationsĪs a utility function, dtreeviz provides cision_boundaries() that illustrates one and two-dimensional feature space for classifiers, including colors that represent probabilities, decision boundaries, and misclassified entities. of San Francisco, where he was founding director of the University of San Francisco's MS in data science program in 2012. ![]()
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