XAI - Cohort analysis for time columns now improved to be more interpretable.
XAI - Cohort analysis now working for tables that are two hops away from the prediction entity table.
A new refit feature enables automatic model refitting on entire data.
Descriptions can now be added and updated for any objects in the Kumo platform.
During new pquery creation, automatically re-use already materialized graphs from prior pQuery creation jobs.
A new connector is available for connecting to Google Cloud BigQuery.
For multilabel classification pQueries (e.g. using the LIST_DISTINCT() operator on a maximum of 1,000 classes), evaluation metrics now include class-specific metrics.
XAI - In Column Analysis, actual versus predicted values are now displayed per column.
A new table column type called Embedding enables the use of embeddings as an input column.
For regression pQueries predicting a numeric output (using COUNT, SUM, etc. operators), evaluation results now include scatter plot charts that display actual versus predicted values.
During pQuery training, charts and tables are now provided to show how the training example target labels used to train the pQuery vary over time and across training/validation/holdout data splits.
A “Distribution of Predictions” chart showcasing a visualization of the predicted values alongside the actual target labels for all entities in a regression task (e.g., predictive queries with COUNT() or SUM() operator).
Expose boolean advanced option to handle prediction of unseen target entities at batch prediction time for link prediction tasks.
Creating custom Kumo Views using SQL queries on top of tables already connected to the platform.
Enable kicking off up to 10 asynchronous jobs (training/batch prediction) that will get queued and run sequentially one after another as older jobs complete.