Jump to content
• To Search the Seeq Knowledgebase:

# Search the Community

Showing results for tags 'training set'.

• ### Search By Tags

Type tags separated by commas.

### Forums

• Community Technical Forums
• General Seeq Discussions
• Seeq Admin Forum
• Training Resources
• Product Suggestions
• Seeq Data Lab
• Community News
• Seeq Blog Posts
• News Articles
• Press Releases
• Upcoming Events
• Resources

### Calendars

• Community Calendar

### Categories

• Seeq FAQs
• Online Manual
• General Information

• Published
• Code
• Media

• 0 Comments

• 0 Replies

• 0 Reviews

• 0 Views

Found 1 result

1. ## Calculating r2 value on a validation set

When you are evaluating the efficacy of a regression, there a few commons methods. You might simply take the difference between your predicted value and your actual value, then create capsules when this value deviates from some critical magnitude. I'll outline an alternative approach, by calculating the r-squared (r2) value over each capsule (in my case, days), but this can be applied to any condition like batches or a Manual Condition of training and validation. The general outline is: 1. Build a prediction in Seeq using the Prediction tool. You can specify your training window by a condition or simply start and end time. More details in our Knowledge Base article: https://support.seeq.com/space/KB/143163422/Prediction 2. Create a condition in which you want to compare R2 values. In this example, I'll simply use a Periodic Condition of days. 3. Resample your predicted value based on your original value. Seeq's resample function allows an input of another signal, which is particularly critical if your model inputs have varying sample rates. This will eliminate any error that would of otherwise been introduced by oversampling of your prediction and interpolation issues. 4. Calculate the R2 value over the condition from Step #2 using the following Formula. \$ym = \$signal.aggregate(average(), \$days, startkey()).toStep() \$total = ((\$signal-\$ym)^2).aggregate(sum(), \$days, startkey()) \$residual = ((\$signal-\$prediction)^2).aggregate(sum(), \$days, startkey()) \$r2 = (1-(\$residual/\$total)).toStep() return \$r2 You can continue your Analysis by building a Value Search for when your R2 deviates below a given threshold - or summarize your results in your Organizer Topic. Feel free to reach out with any questions or improvement ideas! Happy Seeqing! -Chris O, Seeq Analytics Engineer
×

• #### Seeq Links

×
• Create New...