Diviner analysis: Difference between revisions

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File:Load_Val_X_and_Y_Context_Menu.png
File:Load_Val_X_and_Y_Context_Menu.png
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=====Validation Data=====
'''Important Note:''' It is recommended to load validation data, if available, before performing a Diviner run. Doing so will apply all the models to the validation data and report RMSEP values in the Diviner results. If validation data is loaded afterwards then the Diviner run will need to be recalculated to apply the models to the test data.


====Select Calibration Y Column====
====Select Calibration Y Column====
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====Review Outliers====
====Review Outliers====


This section is under construction
If outlier detection is turned on then potential outliers will need to be reviewed. Please see this page for more information: [[diviner_review_outliers | reviewing outliers for Diviner]]


====Review Results====
====Review Results====

Revision as of 09:48, 23 August 2024

Diviner Analysis Window

The Diviner Analysis window serves as the main interface to perform a diviner run.

Main Diviner Interface.png

Using the Diviner Analysis window

Loading Data

There are several methods to load data into the Diviner analysis window. As of version 9.5 the Diviner analysis window does not allow importing data directly from a spreadsheet or other file format. This must be done before hand and the resulting DataSet Object must be present in the Workspace Browser. Please see this wiki page for importing data into: Importing Data and this webinar: Importing Data Webinar

To Load Data

File Menu 1

  1. Go to File → Load Data → Calibration X (or Validation X)
  2. Choose data to load for X block
  3. Go to File → Load Data → Calibration Y (or Validation Y)
  4. Choose data to load for Y block

File Menu 2

  1. Go to File → Load Data → Cal X and Y (or Val X and Y)
  2. Choose data for X block
  3. Choose data for Y block

Load Data File Menu.png

Context Menu 1

  1. Right click on Calibration X block (or Validation X block)
  2. Select Load Data
  3. Choose data to load for X block
  4. Right click on Calibration Y block (or Validation Y block)
  5. Select Load Data
  6. Choose data to load for Y block

Context Menu 2

  1. Right click on Calibration X block or Calibration Y block (or Validation X block or Validation Y block)
  2. Select Load X and Y
  3. Choose data to load for X block
  4. Choose data to load for Y block


Validation Data

Important Note: It is recommended to load validation data, if available, before performing a Diviner run. Doing so will apply all the models to the validation data and report RMSEP values in the Diviner results. If validation data is loaded afterwards then the Diviner run will need to be recalculated to apply the models to the test data.

Select Calibration Y Column

If the Calibration Y block is multivariate then a single Y variable must be selected for modeling:

Method One

  1. Go to Edit → Calibration → Select Y-Column
  2. Select Y variable to use for modeling

Method Two

  1. Right click on Calibration Y block
  2. Choose Select Y Column
  3. Select Y variable to use for modeling

Set Preprocessing Recipes

Once data has been loaded, the next step is to set the Preprocessing recipes to be used by Diviner to build the models. Click on the Preprocessing button to open the Diviner Preprocessing window. Please see this page for more information about the Diviner Preprocessing interface: diviner preprocessing interface. The collection of recipes will be referred to as a library.

Cross-Validation

Review and modify the cross-validation settings using the Cross-Validation button. Please this page for more information on using cross-validation: cross-validation. The default is set to Venetian Blinds with 5 splits.

Outlier Detection

If the preprocessing library contains recipes to use for outlier detection then sse the Outlier Detection On/Off toggle to set if Outlier detection should be performed.

Starting a Diviner

To start a Diviner run, click on the Start Diviner Run button.

Diviner Interface Start Run button.png

Review Outliers

If outlier detection is turned on then potential outliers will need to be reviewed. Please see this page for more information: reviewing outliers for Diviner

Review Results

This section is under construction

Diviner Buttons

This section is under construction