Click to learn more about author Maarit Widmann. A complete time series analysis application covers the steps in a Data Science cycle from accessing to transforming, modeling, evaluating, and deploying time series data. However, for time series data the specific tasks in these steps differ in comparison to cross-sectional data. For example, cross sectional data are […]
Will They Blend? Theobald Meets HANA
Click to learn more about author Maarit Widmann. In the “Will They Blend?” blog series, we experiment with the most interesting blends of data and tools. Whether it’s mixing traditional sources with modern data lakes, open-source devops on the cloud with protected internal legacy tools, SQL with noSQL, web-wisdom-of-the-crowd with in-house handwritten notes, or IoT […]
From Modeling to Scoring: Correcting Predicted Class Probabilities in Imbalanced Datasets
Click to learn more about co-author Maarit Widmann. Click to learn more about co-author Alfredo Roccato. This is the second part of a the From Modeling to Scoring Series, see Part One here. Wheeling like a hamster in the Data Science cycle? Don’t know when to stop training your model? Model evaluation is an important part […]
From Modeling to Scoring: Finding an Optimal Classification Threshold based on Cost and Profit
Click to learn more about co-author Maarit Widmann. Click to learn more about co-author Alfredo Roccato. Wheeling like a hamster in the Data Science cycle? Don’t know when to stop training your model? Model evaluation is an important part of a Data Science project and it’s exactly this part that quantifies how good your model is, […]
From Modeling to Scoring: Confusion Matrix and Class Statistics
Click to learn more about author Maarit Widmann. Wheeling like a hamster in the Data Science cycle? Don’t know when to stop training your model? Model evaluation is an important part of a data science project and it’s exactly this part that quantifies how good your model is, how much it has improved from the previous […]