Author Linda Haviland

This article explains model evaluation techniques, including Lift Chart, ROC Curve, Prediction Distribution graphs, and the Profit Curve tool.

This article covers the options you have when creating a project in DataRobot. There are several tabs within Advanced Options that allow you to customize modeling. 

Learn how to upload actual results to enable DataRobot's model monitoring capabilities to provide more insight into how the model is performing over time.

Learn how machine learning can be used for predictive maintenance in practice. The raw data is captured from a set of 100 NASA turbofan engines.

Before building any models, you will need to ensure your data is set up properly. Let’s take a look at how to structure our dataset so that it’s ready for predictive modeling.

Learn how to use the DataRobot platform to evaluate three different model setups for predicting losses: Frequency-Severity, Tweedie, and Frequency-Cost

Learn how to craft the best possible target when designing a recruiting algorithm. The target feature encodes your definition of success and does not need to be the same definition that other recruiting teams use. See choices and considerations specific to recruiting algorithms.

This post was originally part of the DataRobot Community. Visit now to browse discussions and ask questions about DataRobot, AI Cloud, data science, and more. Even with imbalanced data, DataRobot can build a good model. In this article, we explain imbalanced data and share how to best attack imbalanced data with DataRobot. https://datarobot.wistia.com/medias/xs93zuzd7c Imbalanced data occurs when there is an…