Release notes
Version 2.5
Release Date: August 25, 2020
New Features
The Neuton Neural Framework was updated on the platform. The Neuton Neural Framework is the core of the platform, and the next generation of the framework allows users to create models up to 10 times faster and with enhanced predictive power.
A Model Quality Diagram was added to the platform. It is a graphical representation of model quality in relation to metric indicator values that are scaled in the range [0-1], where 1 is the ideal quality of the model, and 0 is the minimum quality of the model. It also allows users to understand metric balance. When the figure displayed is close to the shape of a regular polygon that conveys a perfect balance between the metric indicator values.
Version 2.4
Release Date: August 5, 2020
New Features
Model-to-Data Relevance Indicator was added to the platform, showing how trustworthy the predictions are. It is determined by evaluating the statistical difference between the data used for training and the data used for predictions.
Text notifications are now available on Neuton Platform. For your convenience, Neuton can inform you about training completion via text notifications, which can be especially useful for trainings with long durations. You have the opportunity to provide the preferred phone number for receiving updates in a pop-up window when training is initiated.
Bug Fixes
Class labels are now displayed as they looked in the training dataset for numeric labels.
Feature Importance Matrix is now available for solutions with enabled “Continue Training” option.
Version 2.3
Release Date: July 27, 2020
New Features
The Model Interpreter allows you to change the feature values and see how prediction result will change in real time. It also shows the feature influence for numeric features, which can help you to understand the influence of feature value on prediction results. And using the interactive settings for the threshold value of the target variable, you can see the values of features for which prediction results are above or under the selected threshold value.
In the new release you can use caret, tab or pipe as a separator for csv files. The full list of supported separators is now: comma, semicolon, pipe, caret and tab.
For preloaded datasets all training settings, except the target metric, are now preconfigured.
Automatic ticket notification was added for all workflow stages.. If you have in issue while working with Neuton, the platform will automatically create a ticket for the Neuton Support Team, and you will be notified via email. In the new release terms of use are also updated for support requests.
Prediction results are now displayed in the same window, instead of in a pop-up window.
Version 2.2.0
Release Date: June 15, 2020
New Features
Confidence interval column was added to prediction results for regression task type for all type of prediction (Web, REST API and Downloadable Solution).
Solution_ID was added to “Solution Details” settings on “My Solutions” page.
New descriptions for preloaded datasets were added to Dataset tab.
Metrics lists were changed for binary and multi class classification task types.
Smart task type detection algorithm was updated and now it detects task type for train dataset in more advanced way.
Version 2.1.0
Release Date: April 27, 2020
New Features
The New Feature Importance Matrix shows the ranking of the most important features based on both the original dataset and the dataset after preprocessing/feature engineering.
Exploratory Data Analysis shows dataset analytic information: data overview, feature categories relations, correlations, time dependencies, missing values, outliers and many other
Neuton Online
Release Date: December 1, 2019
Features:
Neuton Neural Network Framework release__version 3.4
Automated Neuton neural network model architecture creation
Automated hyper parameters configuration
Regression and classification (binary/multi-class) problem solving
Support for large datasets for training
Overfitting prevention
Automated training completion upon reaching optimal model and predictive power
Proprietary automated training and validation sampling
5-fold cross-validation for training
Neuton Auto ML
Intuitive and user friendly workflow for Machine Learning model creation
Automated and manual problem type definition (regression or classification) based on dataset analysis
Automated dataset preparation (preprocessing)
Time series support
Text fields support
Automated feature engineering
Automated training
Automatic training completion upon one of the following conditions:
Creation of the best possible model without overfitting
Time limit (user input) reached
Number of coefficients limit (user input) reached
Manual stop and resume training capability
Support for CSV datasets
Ability to preview uploaded datasets
Ability to download datasets previously uploaded to the platform
Preloaded experimental datasets
Display of other relevant metrics in addition to the target metric
Model feature importance matrix visualization
Predictions
Downloadable models in binary format with Python script-calculator for local use without Neuton
Embedded models capability
Predictions via Web interface
Predictions via REST API
REST API examples in various programming languages s (C++, C#, Python, Java, Scala)
No limitations on input data size for predictions*
*Excluding the Gravity Plan
Infrastructure
Cloud Based solution
SaaS solution
GPU training support
Automated and seamless provisioning of infrastructure necessary to perform training and predictions
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