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Regression
Label for Regression Analysis
Overview
Regression models are used to predict continuous values. They provide a function that determines a relationship between an independent variable and a dependent variable, allowing you to find trends in your data.
Plainsight supports labeling images using a regression label type that supports a numerical value. In the future, an API will be available to supply data for training a regression model.
Labeled image for a regression model
Define a Regression Label
- 1.Navigate to the "Label Definitions" tab of your dataset
- 2.Type label name in the "Name" field
- 3.Choose Regression from "Type" dropdown
- 4.Click "Save" button
Data Format
A Regression schema element creates an array of objects with data consisting of a float value.
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{
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"type": "regression",
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"data": 168,
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"children": {}
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}
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Labeling Regression Data
Once you've defined your Regression label, you can begin annotating your regression data.
Training a Regression Model
You can train a regression model by specifying Regression as the Model Output Type in your SmartML configuration. Check out our walk-through on building your regression model in Plainsight:
Last modified 2mo ago