Changes 0.7.1

• Added a proper user error for missing values in Estimate Uncertainty
• Fixed a bug where regression based conformal predictions was using wrong normalizations in LIME
• Added a progress bar to estimate uncertainty

Changes in 0.7.0

• Added new Operator Interpret Groups that currently calculates the average differences between categories
• Added a new Operator Estimate Uncertainty, which uses Conformal Prediction to estimate the uncertainty of your prediction (Classification or Regression)

Changes in 0.6.0

• Generate Interpretation now supports IOTablePrediction Models. Those are (as of RM 9.11):
  • - Function Fitting
  • - XGBoost
  • - Anomaly Models from Anomaly Extension (version 4.0+)
• Fixed a bug where Generate Interpretation fails if you provide a python based classification model which does not provide a confidence. You now get a proper error message.

 Changes in  0.5.0

• LIME is now not re-scoring the artifical table if you you don't redraw in each iteration. This can speed up LIME for complex and python models by a lot.
• Generate Interpretation now adds an additional column "Interpreted Column" which explains which column was interpreted. This can be very useful for classification problems.
• Added an additional error message when your model does not know it's training label. This can happen in rare cases when you use a Python Learner.

Changes in  0.4.1

•  Added Accumulated Local Effects to Generate Univariate Interpretation
•  Generate Univariate Interpretation's attributes are now called PDP(attributeName) and ALE(attributeName).
• The number of attribtues which are shown in the interpretations column of Generate Interpretation is now configurable.
• Fixed a bug in Generate Interpretation which caused that only the top values and not top absolute values were shown.

Changes in 0.4.0

• Added a new Operator Generate Univariate Interpretation which currently provides PDP as an algorithm.
• Fixed a bug which made Shapely fail on data sets with 2 attributes

Changes in 0.3.0

• Added a new operator Generate Neighborhood which returns the LIME-weighted Neighborhood
• Fixed a major bug in LIME, which made the weights incorrect.

Changes in 0.2.0

* Grouped models are now supported

* Fixed an issue which made the result not reproduceable, even though a static random seed was used

* Fixed a bug which influenced the calculation of LIME on nominals. Nominals were treated like numericals.

Changes in 0.1.1

* Added an icon

* Fixed an error that there was no proper UserError if types of training and testing were different

* Fixed an error that real and integers were considered of different types.