Tutorials¶
- Debugging scikit-learn text classification pipeline
- TextExplainer: debugging black-box text classifiers
- Explaining XGBoost predictions on the Titanic dataset
- Named Entity Recognition using sklearn-crfsuite
- Explaining Keras image classifier predictions with Grad-CAM
- 1. Loading our model and data
- 2. Explaining our model’s prediction
- 3. Choosing the target class (target prediction)
- 4. Choosing a hidden activation layer
- 5. Under the hood -
explain_prediction()
andformat_as_image()
- 6. Extra arguments to
format_as_image()
- 7. Removing softmax
- 8. Comparing explanations of different models