![]() Discrimination Threshold: a visualization of the precision, recall, F1-score, and queue rate with respect to the discrimination threshold of a binary classifier. ![]() Confusion Matrix: a heatmap view of the confusion matrix of pairs of classes in multi-class classification.Classification Report: a visual classification report that displays a model's precision, recall, and F1 per-class scores as a heatmap.Some of our most popular visualizers include: Classification Visualization The primary goal of Yellowbrick is to create a sensical API similar to scikit-learn. In scikit-learn terms, they can be similar to transformers when visualizing the data space or wrap a model estimator similar to how the ModelCV (e.g. Visualizers are estimators - objects that learn from data - whose primary objective is to create visualizations that allow insight into the model selection process. The full documentation can be found at and includes a Quick Start Guide for new users. By applying visualizers to the model selection workflow, Yellowbrick allows you to steer predictive models toward more successful results, faster. For instance, they can help diagnose common problems surrounding model complexity and bias, heteroscedasticity, underfit and overtraining, or class balance issues. Visualizer allow users to steer the model selection process, building intuition around feature engineering, algorithm selection and hyperparameter tuning. Similar to transformers or models, visualizers learn from data by creating a visual representation of the model selection workflow. The library implements a new core API object, the Visualizer that is an scikit-learn estimator - an object that learns from data. Yellowbrick is a suite of visual analysis and diagnostic tools designed to facilitate machine learning with scikit-learn.
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