Question - What are the differences between Supervised and Unsupervised Machine Learning?
Answer -
Supervised learning: The algorithms of supervised learning use labeled data to get trained. The models take direct feedback to confirm whether the output that is being predicted is, indeed, correct. Moreover, both the input data and the output data are provided to the model, and the main aim here is to train the model to predict the output upon receiving new data. Supervised learning offers accurate results and can largely be divided into two parts, classification and regression.
Unsupervised learning: The algorithms of unsupervised learning use unlabeled data for training purposes. In unsupervised learning, the models identify hidden data trends and do not take any feedback. The unsupervised learning model is only provided with input data. Unsupervised learning’s main aim is to identify hidden patterns to extract information from unknown sets of data. It can also be classified into two parts, clustering and associations. Unfortunately, unsupervised learning offers results that are comparatively less accurate.