Diviner review outliers: Difference between revisions
(Created page with "==Outlier Detection== If a Diviner run is started with outlier detection turned on then outlier detection will be performed for each preprocessing method designated by the user. Decisions will need to be made with how to handle samples detected as outliers. This Outlier Detection message will appear along with a plot of the Outlier Detection Survey: <gallery widths=600px heights=700px mode="nolines"> File: Outlier_Detection_Message.png File: Outlier_Detection_Survey_Pl...") |
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[[File: Select_Outlier_Plot_Highlighted_Samples.png | 800px]] | [[File: Select_Outlier_Plot_Highlighted_Samples.png | 800px]] | ||
===Accept Highlighted Samples=== | |||
Once the potential samples are highlighted, use the Accept and Close button (green check icon) to accept this selection and close the figure. These samples will be excluded from the model process. | |||
[[File: Select_Outlier_Plot_Accept_and_Close.png | 400px]] | |||
Revision as of 13:42, 19 August 2024
Outlier Detection
If a Diviner run is started with outlier detection turned on then outlier detection will be performed for each preprocessing method designated by the user. Decisions will need to be made with how to handle samples detected as outliers. This Outlier Detection message will appear along with a plot of the Outlier Detection Survey:
The Outlier Detection Survey shows the number of samples (left axis) and ratio of samples (right axis) per preprocessing method designated to use for outlier detection.
Use the Include All Samples button to keep all samples in the model building process. If you would like to further inspect the samples flagged as potential outliers click on the Inspect Outliers button. This will open the Potential Outlier Status for each Preprocessing plot.
Potential Outlier Status for each Preprocessing
The Potential Outlier Status for each Preprocessing plot will allow selecting samples to be flagged as outliers and thus not used in the model building process.
This plot shows the sample index numbers on the x-axis and the outlier preprocessing methods on the y-axis. Samples that are potential outliers for each preprocessing method are colored pink.
Potential Outlier Status Toolbar
Use the wrench icon to change how the potential outliers are highlighted.
For each outlier preprocessing method you can choose to highlight samples from:
- The robust PLS model
- The robust PCA model
- The union between the robust PLS and robust PCA models. This will give the unique samples from both models.
- The intersection between the robust PLS and robust PCA models. This will give only the samples that are present in both models.
The last option, Highlight Commonly Flagged Samples, allows highlighting the samples flagged as outliers in all of the preprocessing methods.
Accept Highlighted Samples
Once the potential samples are highlighted, use the Accept and Close button (green check icon) to accept this selection and close the figure. These samples will be excluded from the model process.
