Yellowbrick: Steering Machine Learning with Visual Transformers
summary
Yellowbrick is a pure‑Python library that extends Scikit‑Learn with a core Visualizer object so Visualizers fit and transform inside pipelines and produce matplotlib figures. It ships concrete tools: radial and rank2D feature plots, text frequency and t‑SNE views, silhouette and elbow curves for K selection, confusion matrices and residual plots for diagnostics. API details include draw, finalize and poof renders the figure; version 0.4 contains the shown visualizers.
AI-written summary · about
chapters (12)
- 0:00Yellowbrick introduction
- 1:54Speaker background
- 3:20Scikit-learn rise and impact
- 5:46AutoML limitations
- 7:35Visual steering in ML
- 9:03Yellowbrick purpose
- 11:17Feature selection with Yellowbrick
- 14:08Feature/text visualization
- 17:37Model diagnostics
- 21:12Hyperparameter tuning
- 25:33Contributing to Yellowbrick
- 29:25Q&A
related talks
38mBuilding a Visualizer in Yellowbrick
35mVisual Diagnostics for More Effective Machine Learning
39mVisual diagnostics for more informed machine learning
43mVisualising Machine Learning Models
22mVisualizing machine learning algorithms in the Jupyter notebook
30mWords in Space
01h 08mMastering Visual AI with Vision-Language Models & Advanced Evaluation Techniques
01h 00mData, visualization, and designing AI