Regression for tabular data

Regression machine learning algorithms are used to predict continuous numerical values or quantitative response variables based on a set of input features. The goal of a regression model is to estimate the relationship between the input features and the response variable, and to use this relationship to predict new values of the response variable.

Regression models are widely used in various applications, such as stock price prediction, housing price prediction, demand forecasting, and customer lifetime value prediction. For example, a regression model can be used to predict the price of a house based on features such as the number of bedrooms, bathrooms, and square footage.

The performance of a regression model can be evaluated using several metrics, including mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), R-squared, and adjusted R-squared. These metrics help to determine the accuracy of the model in predicting the response variable. For example, the MSE measures the average squared difference between the predicted and actual values, while the R-squared measures the proportion of variance in the response variable that is explained by the input variables.

VIANOPS 2.0 has full support for regression models for tabular data that covers:

• Performance drift monitoring
• Feature drift monitoring
• Prediction drift monitoring
• Bias & fairness analysis and monitoring

Performance metrics tracked in VIANOPS for regression models: mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE).

Go to the Monitor your model How-To guide to learn more details about how to set up monitoring jobs with policies and view monitoring dashboards.

Also, see the free trial tutorial and this blog example of monitoring and analyzing a regression model.