> ## Documentation Index
> Fetch the complete documentation index at: https://kumo.ai/docs/llms.txt
> Use this file to discover all available pages before exploring further.

# 2023 Product Updates

> New updates and improvements

## v1.26 (2023-12-18)

**Summary**

* The pquery syntax has been updated to make it easier to understand and more flexible in the way filters can be applied.

* Various minor fixes and UI improvements.

## v1.25 (2023-11-27)

**Summary**

* BigQuery now available as a batch prediction output.

## v1.24 (2023-11-13)

**Summary**

* New model planner available during pQuery training allows for fine-grained control over encoders, training strategy, and the AutoML search space.

* Additional model planner (previously advanced options) configuration options available.

## v1.23 (2023-10-30)

**Summary**

* XAI: various minor fixes and UI improvements.

* XAI: metrics now available for multiclass and multilabel classification tasks.

* For node prediction tasks, test data splits can now be downloaded from the Review Evaluation Metrics page.

* When selecting source tables, a new raw table option is available for connecting tables that don't conform to either fact or dimension table types.

* Kumo views enable the running of traditional SQL queries that materialize a view in the Kumo data plane.

## v1.22 (2023-10-16)

**Summary**

* Batch predictions now include output statistics computed from a sample of table data.

* Various minor fixes and UI improvements.

## v1.21 (2023-10-02)

**Summary**

* 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.

## v1.20 (2023-09-18)

**Summary**

* 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.

## v1.19 (2023-09-04)

**Summary**

* 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.

* Enable concurrent execution of more than 1 job.

## v1.18 (2023-08-21)

**Summary**

* A plot showcasing the distribution of values for timestamp columns for validating while ingesting new tables.

* S3 CSV data sources supported as connectors.

* Calibrating batch predictions for classification tasks using Platt Scaling.

* Parallelize batch prediction jobs involving large dataset size on multiple workers (up to 4).

* XAI - Explaining how the underlying data contributes to the final predictions.

  * Contribution score of individual tables and the columns within them.

  * Cohort analysis for the range of values of each column and for the range of number of historic facts available in tables.

* Miscellaneous minor UX flow, bug, predictive accuracy fixes.