Regression
Last updated
Last updated
Solving regression problems is one of the most common applications for machine learning models, especially in supervised machine learning. Models are trained to understand the relationship between independent variables and an outcome or dependent variable. The model can then be leveraged to predict the outcome of new and unseen input data, or to fill a gap in missing data.
To build a regression model you collect data as usual, but rather than setting the label to a text value, you set it to a numeric value.
You can use any of the built-in signal processing blocks to pre-process your vibration, audio or image data, or use custom processing blocks to extract novel features from other types of sensor data.
You have full freedom in modifying your neural network architecture - whether visually or through writing Keras code.
See Neural Network Settings on the Learning Block page.
See Neural Network Architecture on the Learning Block page.
See Expert mode on the Learning Block page.
If you want to see the accuracy of your model across your test dataset, go to Model testing. You can adjust the Maximum error percentage by clicking on the "⋮" button.
