Backlog

[interpret-ml]

This issue is to maintain links to all feature requests and bugs in one place. Issues listed here should have either the "enhancement " or "bug" label applied to them.

Issues not listed here are searchable with: is:issue is:open -label:bug -label:enhancement

Enhancements, python

• add "warm_start" parameter to enable stage customization during fitting (Staged prediction/incremental fitting support #304). Also see issue # 403
• add a prediction function that also calculates uncertainty in the prediction (Use standard deviation from global explanation graph to calculate standard deviation of prediction? #235)
• handle mono-classification with Shap (Error: Class Number Mismatch #428)
• avoid resetting the index on call to show after explain_local (Is there a way to show explain_local without resetting the index? #72)
• user defined labels for local explanation samples (How to add index names--(strings such as ) #300)
• change boosting algorithm to stop it from producing graphs with large positive or negative scores in some regions (Can I limit magnitude of feature curves #122)
• ranking (Explaining learning to rank tasks #181)
• Dask arrays for large datasets (Large datasets: support for Dask arrays? #249)
• progress indication during training (Verbose option during training #252)
• continuous output of metrics during training (adding verbose during training #7)
• Spyder support for show function (Show(hist), Spyder3.8, testing on private data  #191)
• UI to show missing value scores (Doing some treatment for missing values #18)
• show error bars for interactions in the UI, perhaps with a hover (p-value of the functions of features/feature interactions #127)
• Improve contrast of interaction heatmaps (Improve contrast of interaction heatmaps #236)
• dashboard data disappears when changing tabs (Data shown in the Dashboard disappears when navigating across the tabs #106)
• add permutation Importance (Feature importance using Permutation #26)
• alternative feature importance techniques (Observing Alternative Feature Importance Techniques #374)
• add categorical support to Morris Sensitivity Analysis (PCA and Categoricals Interpretability Issue #275)
• alternate base estimators such as linear models, polynomial regression, or splines (Non-Treebased base models #185)

Enhancements, R

• local explanations in R ([r] ebm_show local version? #222)
• R multiple new features (R: interpret package features #294)
• add threading to R and in general move the parallelism into C++ (Can't use n_jobs argument in R package #180)

Bugs

• predictions in R different from python (R: intercept may not be handled correctly #417)
• cannot create multiple visualizations in Databricks (Databricks Render multiple visualizations #88)
• DataException: MissingColumnsInData, Expected column(s) 0 not found in fitted data (DataException: MissingColumnsInData, Expected column(s) 0 not found in fitted data #209)
• RandomizedSearchCV doesn't work with InterpretML LogisticRegression (Logistic Regression and Randomized Search CV #301)
• categorical visualize bug (visualize has a bug for categorical features  #119)
• lime results may differ between InterpretML and LIME package (Interpret's LIME is not the same as original LIME #133)
• multiclass LIME (multiclass problem #306)
• LimeTabular seems to require Pandas dataframe for categoricals (Getting error while calling LimeTabular with ColumnTransformation  #322)
• Categorical features in PartialDependence (Categorical features in PartialDependence()? #217)
• show does not display any output in some settings (Show Displays Resulting in Blank Cell Output - Dash mode = "external"? #229)
• ClassHistogram().explain_data() having index error (ClassHistogram().explain_data() having index error.... #274)
• raise an exception when the multi-model dashboard is used inside the InlineProvider (Dashboard not showing results #333)
• in some scenarios showing multiple EBM dashboards could fail (Unable to show multiple Explanation Dashboard in the same notebook #350)
• feature values above 999000000000000 cannot be visualized (Error in showing global explainations when using more features #370)