May 11, 2023
ML Integration with Plans
Added integration with Plans. Use Plans to add new data from a workflow to an existing Machine Learning project. Learn more about ML integration with Plans.
Faster Data Insights
Runtime Upgrade Required
To use this feature, you must upgrade your project to AYX ML Runtime version 2.19.0.
Added the Correlations and Outliers panels from Data Insights to Problem Setup. You can now quickly view insights about your data before selecting a problem type.
May 4, 2023
Simplified Modeling Workflow
Reduced the number of modeling workflow stages. You can find the previous Model Setup and Feature Engineering options in the Auto Model stage.
Streamlined Model Evaluation
Redesigned the General tab to highlight metrics that drive business outcomes. Combined the Performance and Advanced Insights tabs to house all advanced metrics.
April 27, 2023
Time Series Prediction Intervals for All Models
Added prediction intervals for all Time Series models in the forecast graph. Additionally, the downloadable forecast CSV file includes the prediction intervals.
April 20, 2023
Python Model Export
Runtime Upgrade Required
To use this feature, you must upgrade your project to AYX ML Runtime version 2.16.0.
Added the option to export your model as Python code in the Export and Predict stage. Export your model to evaluate the underlying code.
April 6, 2023
New Landing Page
Renamed the Project Page to the Landing Page and updated the page design. Updates include a new page header, table styling, and a Machine Learning project icon. There is no change to existing functionality.
New Navigation to the Landing Page
Previously, selecting All Projects would navigate to the Project Page (now called the Landing Page). Now, select the Machine Learning icon in the upper-left corner of the page to navigate to the Landing Page.
Example Use Cases
Added a link to Example Use Cases in AYX ML to the Additional Resources section of the Resources Pane. You can find the Resources Pane on the new Landing Page.
March 30, 2023
New Problem Setup Stage
Streamlined the data prep and model selection process. You can now easily see your data and choose a machine learning method at the same time. Problem Setup replaces the Data Prep stage.
Time Series Prediction Intervals
Added prediction intervals to Arima, Prophet, and Exponential Smoothing model types. To view the prediction intervals, go to the Time Series Forecast Graph located in the Export and Predict stage . Use prediction intervals to determine the confidence of a forecasted data point.
February 9, 2023
You can now easily select the columns you want to include in the modeling process and change column data types. To use this feature, select Manage Columns in the Prep Data stage.
January 31, 2023
Runtime Upgrade Required
To use these features, you must upgrade your project to AYX ML Runtime version 2.4.1.
Data and Feature Anomaly Checks Added to Export and Predict
Added a message to inform you if your data for prediction contains categorical values that were not present in your training data . The message details what those values are, and which columns contain them.
Customizable Feature Selection
Added the ability to select which features you use in your model. This includes engineered features. To customize your feature selection, go to the Features tab in the Feature Engineering step . Reducing your feature count can speed up the modeling process.
Partial Dependence Plot Performance Improvements
Improved the speed of populating the Partial Dependence plot. On average, you can expect 50% faster load times.
December 8, 2022
Added the ability to upgrade individual projects to the latest AYX ML Runtime Version—and to revert back to your most recent version if you change your mind. You can find this functionality in the 3-dot menu on each project on the project page.
- Project versioning ensures that changes to modeling algorithms DO NOT impact business results unless you select an upgrade. This provides a consistent experience when you revisit a previous project.
- Note that the AYX ML Runtime Version upgrade only applies to model tuning improvements made by Alteryx. Improvements to the UX, new features, security, and infrastructure update automatically.
December 1, 2022
Time Series Enhancements
What Is Time Series
Time Series regression expands your modeling capabilities with data that includes a time component. Now, you can forecast into the future and get accurate predictions. Do things like demand forecasting, financial forecasting, and more with Alteryx Machine Learning.
Alteryx Machine Learning uses commonly used and state-of-the-art time series models. These include Facebook Prophet, ARIMA, and ETS, in addition to other regression models such as XGBoost and LightGBM.
Why Time Series Matters
Use Time Series for future-looking, time-based predictions. This empowers you to quickly leverage prior data to forecast future outcomes. Enhanced capabilities account for trending and seasonality, making model performance stronger.
Time Series now includes 2 major enhancements to the Machine Learning experience:
Decomposition [Model Setup step]: Visualize trend and seasonal signals in isolation from the residual signal. This allows the non-time series specific models to perform better in most cases (where time-series specific models are Facebook Prophet, ARIMA, and ETS). We run all models with and without decomposition and then display the best model on the leaderboard. We support Decomposition Visualizations for these frequencies:
Time Series Forecast Graph and Data Export [Export and Predict step]: Introduced line graph and graph data to visualize and then use forecasted data.
Changes to Existing Workflow
For a clearer workflow, we moved items from Data Insights to a new Model Setup step. Note that this doesn’t represent new functionality, just a new organizational flow.
August 25, 2022
Feature Engineering Step
Added an automated Feature Engineering step to Alteryx Machine Learning. This step allows you to apply primitives (generalized operations) to calculate new features. These features can improve model performance and help you gain further insight. Additionally, you can view the correlation values for the engineered features. Look for this new feature on the left side of the Alteryx Machine Learning interface.
August 2, 2022
Updated Integer Parsing
We extended support for parsing real numbers (for example, 2.45 and 3.0) as integers. The parsing truncates the right side of the decimal point. For example, 2.45 becomes 2, and 3.0 becomes 3.
New Time Series Primitives
Added "Fourier Transform" and "Rolling Trend" primitives for improved Time Series feature engineering. We also added an additional 16 DateTime primitives for feature engineering.
Custom Input for DateTime Formats
Added 20 new DateTime formats and the ability to enter your own custom format. You can now use more DateTime datasets with our Time Series Regression model.
Machine Learning Predict Tool—Data Validation for Time Series Models
Added Time Series data validation to the Machine Learning Predict tool in Designer. The data validation checks if your input data length is longer than the forecast horizon.
Correlation Tab—Switch X-Y Axes on 2-Variable Plot
Added an option to switch the axes of the 2-Variable Plot in the Correlations tab.
July 21, 2022
Expanded Pipeline Highlights in the Auto Model leaderboard for categorical and numerical operations. We now sort operations by order of execution in the model pipeline. Use this information to choose a model based on which operations we use in the model.
June 23, 2022
Time Series—Irregularly Spaced Data
Support added for irregularly spaced Time Series datasets.
Time Series—Data Checks
Added data checks to warn you when we modify your data.
June 16, 2022
Stop Criteria—Model Setup
You now have the option to set "time" as the stop criteria for model search completion. The minimum stop time is 1 minute.
April 18, 2022
Facebook® Prophet Estimator
Added Prophet to improve predictions for Time Series Regression models. This addition targets datasets with trends and seasonality.
Added ARIMA Regressor for Time Series Regression. ARIMA is a high performant statistical algorithm for Time Series datasets.
Select Completed Models
Updated the Stop button in the Auto Model search step. You can now stop the model search and select completed models up to that point.
Updated the Features panel in the Auto Model step to show all features contributing to your model. This helps you understand and evaluate models based on the features in use. In addition, features used in ensemble models are also shown.
March 17, 2022
Unary Column Detection
A new data check identifies unary data type columns and automatically recommends next steps. This enables you to quickly detect columns to exclude from Machine Learning. The No Variance Data Check flags columns that contain only 1 unique value and then provides you with a recommendation to drop those columns.
February 2, 2022
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