In TensorFlow Playground, what does the color of the dots in the output layer represent?

The color of the dots in the output layer of TensorFlow Playground represents the prediction results based on initial values. In this interactive visualization tool, each dot corresponds to a data point and its color indicates the predicted class that the model assigns to it after going through the neural network. The colors typically denote different classes in a classification task, helping users visually assess how well the model is performing in its predictions.

This approach allows users to quickly see which areas of the input space are correctly classified and where misclassifications occur, providing insight into the model's behavior and the effectiveness of the training process as it interacts with the provided data. Understanding this aspect of TensorFlow Playground is critical for evaluating and improving machine learning models, as it centers on how well the model can distinguish between different categories based on the given input variables.