Which technique reduces the dimensionality of data by combining features into fewer components?

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Multiple Choice

Which technique reduces the dimensionality of data by combining features into fewer components?

Explanation:
Dimensionality reduction is the process of reducing the number of variables under consideration by combining features into a smaller set of components while preserving as much information as possible. It does this by creating new features that are combinations of the original ones, often designed to capture the most important structure in the data. A common approach, like principal component analysis, forms components that explain the maximum variance, so the data can be represented with fewer dimensions without losing too much detail. This helps simplify models, reduces noise, and makes patterns easier to visualize. Other options don’t fit because they describe completely different activities: footprinting is about gathering information about a target system, social engineering involves manipulating people to reveal sensitive data, and tracert is a network diagnostic tool that shows the path data takes to a destination.

Dimensionality reduction is the process of reducing the number of variables under consideration by combining features into a smaller set of components while preserving as much information as possible. It does this by creating new features that are combinations of the original ones, often designed to capture the most important structure in the data. A common approach, like principal component analysis, forms components that explain the maximum variance, so the data can be represented with fewer dimensions without losing too much detail. This helps simplify models, reduces noise, and makes patterns easier to visualize.

Other options don’t fit because they describe completely different activities: footprinting is about gathering information about a target system, social engineering involves manipulating people to reveal sensitive data, and tracert is a network diagnostic tool that shows the path data takes to a destination.

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