Model Repository

Unsupervised learning involves training a model with an unlabeled dataset, where output labels are absent. The primary aim of unsupervised learning is to uncover inherent patterns or structures within the data, without any prior knowledge of what the expected output should be. During training, the model endeavors to identify patterns, regularities, or clusters within the data by grouping similar data points together while distinguishing dissimilar ones.

In supervised learning, a model is trained using a dataset that includes both input features and corresponding output labels. The objective is for the model to learn how to accurately map input features to their corresponding output labels, enabling it to make accurate predictions for new, unseen data. During the training process, the model is exposed to input features along with their associated output labels, and it adjusts its internal parameters to minimize the disparity between its predictions and the correct output labels.