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.
Number of training cycles: Each time the training algorithm makes one complete pass through all of the training data with back-propagation and updates the model's parameters as it goes, it is known as an epoch or training cycle.
Learning rate: The learning rate controls how much the models internal parameters are updated during each step of the training process. Or you can also see it as how fast the neural network will learn. If the network overfits quickly, you can reduce the learning rate
Validation set size: The percentage of your training set held apart for validation, a good default is 20%
Auto-balance dataset Mix in more copies of data from classes that are uncommon. Might help make the model more robust against overfitting if you have little data for some classes.
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 "⋮" button.
