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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.
{
	"type": "regression",
	"data": 168,
	"children": {}
}
Labeling Regression Data
Once you've defined your Regression label, you can begin annotating your regression data.
Regression Labeling
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:
Walk-through: Training a Regression Model
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Multi Classification

Sublabels

Last modified 1yr ago