The main differences between Supervised and Unsupervised learning are given below:
Supervised Learning | Unsupervised Learning |
---|---|
Supervised learning algorithms are trained using labeled data. | Unsupervised learning algorithms are trained using unlabeled data. |
Supervised learning model takes direct feedback to check if it is predicting correct output or not. | Unsupervised learning model does not take any feedback. |
Supervised learning model predicts the output. | Unsupervised learning model finds the hidden patterns in data. |
In supervised learning, input data is provided to the model along with the output. | In unsupervised learning, only input data is provided to the model. |
The goal of supervised learning is to train the model so that it can predict the output when it is given new data. | The goal of unsupervised learning is to find the hidden patterns and useful insights from the unknown dataset. |
Supervised learning needs supervision to train the model. | Unsupervised learning does not need any supervision to train the model. |
Supervised learning can be categorized in Classification and Regression problems. | Unsupervised Learning can be classified in Clustering and Associations problems. |
Supervised learning can be used for those cases where we know the input as well as corresponding outputs. | Unsupervised learning can be used for those cases where we have only input data and no corresponding output data. |
Supervised learning model produces an accurate result. | Unsupervised learning model may give less accurate result as compared to supervised learning. |
Supervised learning is not close to true Artificial intelligence as in this, we first train the model for each data, and then only it can predict the correct output. | Unsupervised learning is more close to the true Artificial Intelligence as it learns similarly as a child learns daily routine things by his experiences. |
It includes various algorithms such as Linear Regression, Logistic Regression, Support Vector Machine, Multi-class Classification, Decision tree, Bayesian Logic, etc. | It includes various algorithms such as Clustering, KNN, and Apriori algorithm. |