What is the main difference between classification and clustering?

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The main difference between classification and clustering is the nature of the output. Classification is a supervised learning technique that entails training a model on pre-labeled data in order to make future predictions, while clustering is an unsupervised learning approach that does not rely on labeled information and instead looks for patterns or similarities among different objects/data points.

Additionally, classification tasks focus more on individual entities; for example categorizing items into predefined classes such as “spam” or “not spam”, whereas clustering uses larger sets of data to group similar observations together without necessarily ascribing labels to each resulting cluster. In summary, both techniques can be powerful tools when it comes to analytical problem solving but their application depends heavily upon the specific task at hand – whether one requires discrete categories or high-level trends across large amounts of input data.