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ML Concepts - Best Practices when using ML Regression Metrics

On this weekly Office Hours for Oracle Machine Learning on Autonomous Database, Jie Liu, Data Scientist for Oracle Machine Learning, covered the best practices when using Machine Learning Regression metrics. The goal of a regression task is to build models based on features to predict a target quantity, that is, a numeric value. After a regression model is applied to a test set, the next step is to evaluate the model performance by checking the error between the regression output and the true value. A certain set of metrics is often used to evaluate the regression model such as mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE) and R squared, etc. What are the differences among those metrics? How does one choose a metric correctly? How does the metric translate back to the business world? In this Session, Jie went through various regression metrics and addressed the questions above. Moreover, he showed how to compute regression metrics in a scalable way using OML4Py. The Oracle Machine Learning product family supports data scientists, analysts, developers, and IT to achieve data science project goals faster while taking full advantage of the Oracle platform. Video Highlights: 00:48 Topics for today 01:10 Upcoming Sessions 02:40 Upcoming Sessions early 2022 03:58 Regression Metrics: Agenda Outline 04:17 Regression Metrics: Motivation 04:51 Plot: Target vs Prediction 07:06 Plot: Residual plot 10:20 Mean Squared Error and Root Mean Squared Error 11:45 Mean Absolute Error 12:05 Experiment: MSE/RMSE vs MAE 13:03 Discussion: Weight on Large Error 16:17 R-Squared and Adjusted R-Squared 19:45 Demo on OML Notebook 25:13 Q&A

Workshop Info

Session Has Completed - 16 November 2021
30 Minutes
Oracle Machine Learning Office Hours
English
Oracle Machine Learning