> ## Documentation Index > Fetch the complete documentation index at: https://docs.edgeimpulse.com/llms.txt > Use this file to discover all available pages before exploring further. # Regression 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. ## Prerequisites ### Labeling 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.

### Processing blocks 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.

## Train your regression block You have full freedom in modifying your neural network architecture - whether visually or through writing Keras code.

## Neural Network settings See [Neural Network Settings](/studio/projects/learning-blocks#neural-network-settings) on the Learning Block page. ## Neural Network architecture See [Neural Network Architecture](/studio/projects/learning-blocks#neural-network-architecture) on the Learning Block page. ## Expert mode See [Expert mode](/studio/projects/learning-blocks#expert-mode) on the Learning Block page. ## Test your regression model 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.

## Additional resources * [Predict the Future with Regression Models](https://edgeimpulse.com/blog/predict-the-future-with-regression-models) * [Estimate Weight From a Photo Using Visual Regression in Edge Impulse](https://edgeimpulse.com/blog/estimate-weight-from-a-photo-using-visual-regression-in-edge-impulse)