Supervised and Unsupervised learning are types of Machine learning.
Supervised learning is based on the supervision concept. In supervised learning, we train our machine learning model using sample data, and on the basis of that training data, the model predicts the output.
Unsupervised learning does not have any supervision concept. Hence, in unsupervised learning machine learns without any supervision. In unsupervised learning, we provide data which is not labeled, classified, or categorized.
Below are some main differences between supervised and unsupervised learning:
|In supervised learning, the machine learns in supervision using training data.
|In unsupervised learning, the machine learns without any supervision.
|Supervised learning uses labeled data to train the model.
|Unsupervised learning uses unlabeled data to train the model.
|It uses known input data with the corresponding output.
|It uses unknown data without any corresponding output.
|It can be grouped into Classification and Regression algorithms.
|It can be grouped into Clustering and Association algorithms.
|It has more complex computation than Unsupervised learning.
|It has less complex computation than supervised learning.
|It provides more accurate and reliable output.
|It provides less reliable and less accurate output.
|It can also use Off-line data analysis.
|It uses real-time data analysis.