5.1.2 Released on 8/2/2021
- Login Screen and Welcome Splash updated.
- The table listings in Data Manager (datasets), Insight (charts etc.), Predict (models) and Optimize (scenarios) now use the full real-estate of the screen and any change to the bottom right corner 'Show rows' option (25,50,100 etc.) will be remembered.
- Browse Data can now simultaneously sort multiple columns.
- Browse Data Actions now provide the choice of columns to show, the order to show them and will retain the users settings over time.
- Additional Pivot settings are now saved including 'Analysis Style', 'Aggregation Type', 'Metrics' along with 'Column', 'Row' and 'Unused Dimensions'.
- Improved error capturing when importing data.
5.1.1 Released on 5/7/2021
- The aggregation step now allows you to pivot data from rows into columns, including summarizing on the pivoted data. This is useful when you want to aggregate your data at a certain level and have a field in your data with multiple values that you would like to turn each value into a new field.
- See LityxIQ Support Site ‘Defining a Pivot within the Aggregation Step’ for more detailed information on how to utilize this new functionality.
- To see this new functionality in action, please view our video demonstration
5.1.0 Released on 1/24/2021
- New aggregation functionality includes creating percent of records and percentage of sums, as well as unique value counts, by aggregation group.
- Options are now available to import SAS, SPSS, or Stata datasets.
- DeepNet algorithm is now fully released
- Jobs will process batches of records in parallel if allowed by the LityxIQ instance setup.
- New option to select the version/iteration of the model to use for the scoring job. Models in production will still always use the official production version. Note that this means that a model not in production can have multiple scoring jobs each using a different version/iteration.
- New option (on the Advanced tab) to allow a scoring job with no records passed in to process without error. Currently, if no records are in the dataset to be scored, the scoring job throws an error. That will continue to be the default operation, but the user can now specify that it is ok for there to be zero records.
- LityxIQ now includes a Gurobi-enhanced upgrade, allowing for solving of Optimization problems using a Gurobi solver.
- Updates and improvements to the user-interface, including detailed user-oriented explanation of constraint definitions.
5.0.4 Released on 9/27/2020
- Additional catching and attempted fixing of import errors, and provision of detailed information on the data line causing any import errors.
- Variable names can now contain up to 128 characters (from 100).
- Imported datasets provided with more than 128 characters in a variable name will be automatically reduced to the first 128 characters to become legal.
- DeepNet neural net algorithm is now available to all users in Beta testing. Please provide feedback on your use of the algorithm in your machine learning models.
5.0.3 Released on 8/31/2020
- Additional algorithm documentation
- The DeepNet neural net algorithm is available to select users in Alpha testing.
- Additional performance metrics are computed for both continuous variable models and binary classification models. See Performance Analysis Metrics to Analyze Classification Models and https://support.lityxiq.com/050028-Performance-Analysis-Metrics-to-Analyze-Numeric-Prediction-Models for a complete list of metrics computed by LityxIQ.
5.0.2 Released on 8/6/2020
- Addition of powerful fuzzy matching functions (in addition to existing regular expression pattern matching functions) to Data Manager.
- An interactive ROC curve and error cost analysis is available for all binary classification models. See https://support.lityxiq.com/364317-C-Statistic-and-ROC-Curves for more information.
- The XGBoost machine learning algorithm is now available to all users
Please do a SHIFT-CTRL-R refresh in your browser to take advantage of the ROC curve analysis functionality.
5.0.1 Released on 5/27/2020
- Automated Insights are now available. For any dataset in LityxIQ, simply select up to five variables for which to generate automated insights each time the data is refreshed. Automated Insights include a ranking of which other variables are important predictors of the target, and provide an easy-to-understand multivariate segmentation for each of the selected targets.
- The export dataset option allows the selection of an escape character for use in the export file.
5.0 Released on 4/1/2020
- Dataset metadata is now computed in parallel to dataset execution being finalized. This provides much-improved execution times for certain large datasets.
- Database schedules now allow for hourly jobs
- Google BigQuery and Amazon DynamoDB are now available as data sources. See Data Connections
- Variable importance scores and other pre-processing information about machine learning model runs is now available for all algorithms