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November 16, 2021

16:00 UTC   Start Times Around the World

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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

Your Experts

    Jie Liu

    Jie Liu

    Jie Liu is a data scientist. He works with Oracle Machine Learning Product Management team to develop marketing content for OML products and deliver data science solutions for customers inside and outside Oracle. Before joining Oracle, he was a data scientist in Epsilon developing machine learning driven real time bidding strategy and application for online advertisement. He obtained his PhD in Electrical Engineering from University of Notre Dame.
    Marcos Arancibia

    Marcos Arancibia   

    Marcos Arancibia is a Senior Principal Product Manager in the Oracle Autonomous Database team. He is charted with developing a comprehensive platform for enabling all customers and use cases to be successful on Autonomous Database, and working with developers to bring their Applications and workloads as well. Within Product Management he works to develop product strategy, roadmap prioritization, product positioning and product evangelization, working closely with the engineering teams in defining the product roadmap for Autonomous Database. He previously was a PM for Oracle Machine Learning, and before joining Oracle in 2010 he spent 13 years at SAS Institute Inc., from Country Manager in LAD to Regional Data Mining lead in the US. He holds a bachelor's degree with additional courses in the master's degree, both in Statistics from UNICAMP in Brazil. He has Certifications from Stanford on AI and Machine Learning, and from the University of Washington on Computational Neuroscience.
    Sherry LaMonica

    Sherry LaMonica

    Sherry is a member of the Oracle Machine Learning Product Management team. She has 20 years of software experience focused on enabling the commercial use of the open-source data analysis software systems R and Python for data science and machine learning projects. She has worked with customers in fields as diverse as as pharmaceutical research, financial analysis, manufacturing and healthcare IT.

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