The Model Interpreter is a tool that allows you to visually see the logic, direction and the effects of changes in individual variables in the model. It also shows the importance of these variables in relation to the target variable.

After the model has been trained, the platform displays a chart with the 10 features that had the most significant impact on the model prediction power. You can also select any other features to see their importance. FIM also has 2 modes, displaying either only the original features or the features after feature engineering. For classification tasks you can see the feature importance for every class.

Feature Influence Indicator shows feature influence on each prediction.

Exploratory Data Analysis (EDA) is a tool that automates graphical data analysis and highlights the most important statistics in the context of a single variable, overall data, interconnections, and in relation to the target variable in a training dataset. Given the potentially wide feature space, up to 20 of the most important features with the highest statistical significance are selected for EDA based on machine learning modeling.

The Confidence Interval, for regression problems, shows in what range the predicted value can change and with what probability.

Extensive list of supported Metrics allows users to evaluate the model from different angles. There are 6 metrics for regression: Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Root Mean Squared Logarithmic Error (RMSLE), Coefficient of Determination (R2), Mean Squared Error (MSE), Root Mean Squared Percentage Error (RMSPE), 9 metrics for binomial classification: Accuracy, Balanced Accuracy, Precision, Recall, F1 Score, Gini, ROC AUC, Lift, and 9 metrics for multinomial classification: Accuracy, Balanced Accuracy, Macro Average Precision, Weighted Average Precision, Macro Average Recall, Weighted Average Recall, Macro Average F1 Score, Weighted Average F1 Score.

Model-to-data Relevance Indicator calculates the statistical differences between the data uploaded for predictions and the data used for model training. Significant differences in the data may indicate metric decay (model prediction quality degradation).

Historical Model-to-data Relevance is an excellent signal for models to retrain. This indicator is designed even for downloadable models, which allows to manage a model lifecycle even outside the platform.

Model Quality Diagram simplifies the process of evaluating the quality of the model, and also allows users to look at the model from the perspective of various metrics simultaneously in a single graphical view. We offer an extensive list of metrics describing the quality popular in the data science community.

Validate Model on New Data Validate Model on New Data shows model metrics on new data to help determine whether the model should be retrained to reflect the statistical changes and dependencies in new data. It also shows metrics in multidimensional space (Model Quality Diagram).

You can download a Neuton model and use it online or offline without subscription. The archive contains the model in binary format and other supporting scripts and objects that, along with a simple python script, pulls it all together when predicting on new data. The downloadable model is absolutely free to use after the initial download, and requires no further access to the Neuton Platform nor license key.

Unlike some competitors Neuton works with time series and text data. While there are additional settings for the Time Series analysis, our NLP module is absolutely automated. If a text column is present in your data, it will be processed according to NLP best practices to date.