Firenze11
Firenze11
[advanced] auto-suggesting performance-based slicing boundaries to maximize cumulative kl-divergence
There should be a way to calculate the performance-based decision boundary that maximizes the cumulative distribution difference in features.
#### Steps to Reproduce * remove "@score:model_1" from "Cluster by" selection box * reduce "Number of clusters" from 4 to 3  #### Expected Behavior Expect to see the clear-cut...
- [ ] add readme to npm homepage https://www.npmjs.com/package/@mlvis/manifold - [ ] add publishing script to Travis CI - [ ] auto bump version in the main package.json (currently `ocular-publish`...
if there are null or undefined values in input data, current behavior is to remove the feature from the list. Desired behavior is: - create a NA category for categorical...
Currently we have 2 ways to show numerical values over spatially distributed data: 1. Overlapping heatmaps, used for displaying two data slices on a map: 2. Hexagon binned heatmaps, used...
Right now only unit tests for each functions are in place. It would be ideal to add integration tests considering the signature of individual functions might change during the development...
When there is a value concentration in numerical feature values (due to filling null values etc), the distribution difference will be overshadowed by the concentration effect.  Possible ways to...
Current implementation involves converting tensor data between array and tf.Tensor format through synchronized data reading, which blocks the UI thread until the values are ready, which can cause performance issues....