According to Microsoft Documentation :
Regression is a form of machine learning that is used to predict a digital label based on the functionality of an item. For example, suppose Adventure Works Bikes is a business that rents bikes in a city. The company could use historical data for an older model that predicts daily locate demand to make sure enough staff and bikes are available.

Classification is a form of machine learning used to predict what category, or class, an item belongs to. For example, a clinic can use a patient’s characteristics (such as age, weight, blood pressure, etc.) to predict whether the patient is at risk for diabetes. In this case, the patient’s characteristics are traits, and the label is a classification of 0 or 1, representing non-diabetic or diabetic.

Clustering is a form (non-supervised) of machine learning used to group items into clusters or clusters based on the similarities in their functionality. For example, a botanist can measure plants and group them based on similarities in their proportions.

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Regression:
Definition: Regression is used to predict a specific numeric value based on input data.
For example: Predict house price based on area, number of rooms, location, etc.

Classification:
Definition: Classification is used to classify data into different groups.
For example: Classify emails as spam or non-spam based on content and title.

Clustering:
Definition: Clustering is used to group similar data points into clusters.
For example: Group customers into groups based on purchasing behavior.
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a very good interview question distinguishing Regression vs classification and clustering. I can answer this question as follows. Regression and Classification are types of supervised learning algorithms while Clustering is a type of unsupervised algorithm. When the output variable is continuous, then it is a regression problem whereas when it contains discrete values, it is a classification problem. flappy bird

If you use the PAM clustering algorithm with an outlier, as in your example, is it possible that PAM would assign the outlier to its own group? Read More

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Using machine learning, classification may be used to make predictions about what kind of group an object belongs to. A clinic, for instance, may ascertain a dordle patient’s risk for diabetes based on demographic information such as age, weight, blood pressure, and other parameters. In this situation, the label is a categorization of the patient as either “non-diabetic” or “diabetic,” and the patient’s features are attributes.