Model Sandbox

In supervised learning, the model is trained on a labeled dataset, where each example is labeled with the correct output. The goal of supervised learning is to learn a mapping between the input features and the output labels, so that the model can accurately predict the output for new, unseen examples. During training, the model is presented with input features and the corresponding output labels, and it adjusts its internal parameters to minimize the difference between its predicted output and the correct output label.

In unsupervised learning, the model is trained on an unlabeled dataset, where the output labels are not available. The goal of unsupervised learning is to discover patterns or structure in the data, without any prior knowledge of what the output should be. During training, the model tries to find regularities or clusters in the data, by grouping similar examples together and separating dissimilar examples. Unsupervised learning is often used for tasks such as data clustering, dimensionality reduction, and anomaly detection.