What is the difference between supervised learning and unsupervised learning?
What is the difference between supervised learning and unsupervised learning?
21228-May-2023
Updated on 29-May-2023
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What is the difference between supervised learning and unsupervised learning?
Aryan Kumar
29-May-2023Supervised learning and unsupervised learning are two of the most common types of machine learning. In supervised learning, the model is trained on a set of labeled data, which means that each data point has a corresponding output. The model then learns to predict the output for new data points. In unsupervised learning, the model is trained on a set of unlabeled data, which means that the model does not know what the output should be. The model then learns to find patterns in the data.
Here is a table that summarizes the key differences between supervised learning and unsupervised learning:
Supervised learning is typically used for tasks where the output is known, such as classification and regression. Unsupervised learning is typically used for tasks where the output is unknown, such as clustering and dimensionality reduction.
Here are some examples of supervised learning tasks:
Here are some examples of unsupervised learning tasks:
Supervised learning is typically more accurate than unsupervised learning, but it requires more labeled data. Unsupervised learning is typically less accurate than supervised learning, but it can be used with unlabeled data.
The best type of machine learning to use depends on the specific task. If the output is known and there is a large amount of labeled data available, then supervised learning is the best choice. If the output is unknown or there is not enough labeled data available, then unsupervised learning may be a better choice.