- $309 Monthly Payment
- App allows users to work with default datasets for fitting and explaining regression and binary classification models. Users can also upload their own datasets, up to 1 GB in size.
- Four choices are available to fit a regression model: Linear Regression, XGBoost Regressor, Random Forest Regressor, and Decision Tree Regressor. Four choices are also available to fit a binary classification model: Logistics Regression, Random Forest Classifier, Decision Tree Classifier, and XGBoost Classifier.
- SHAP values are available to explain global and individual predictions from XGBoost, Random Forest, and Decision Tree algorithms. Trees behind these predictions can also be visualized. Feature Importance is available for all the algorithms.
- SHAP values provide narratives for individual predictions by outlining the enhancers and inhibitors of each prediction, i.e., how each feature value in the observation affects the prediction outcome.
- Feature importance provides the overall dataset level impact of each feature on predictions. In the case of Linear Regression, the feature coefficients are the feature importance values.
- SHAP dependency plots enable users to identify outliers in their predictions so that they can potentially act on these outlier-based insights.
- Users can download the predictions and SHAP values, among other outputs.
For additional product information please contact App’s product manager Vinh Nguyen at firstname.lastname@example.org
Explainable (XAI) Web App