Classification problems involve using only categorical inputs to predict a categorical output.

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BlackTom AI · Accepted Answer

The statement claims that classification problems involve using only categorical inputs to predict a categorical output. This is not accurate.

First, consider the possibility that the inputs could be numerical as well as categorical. Many classification tasks use a mix of feature types, such as age (numerical), income (numerical), and category labels (categorical). Algorithms like logistic regression, random forests, gradient boosting, and neural networks can handle both numerical and categorical features (often after appropriate encoding for categorical data).

Second, the predicted target in classification is indeed a categorical label, but that does not imply that inputs must be exclusively categorical. The output being categorical is the defining trait of classification, while the input feature types are not restricted to one kind.

Third, some special cases exist where inputs are purely numerical (for example, classifying iris species using sepal/petal measurements). This further demonstrates that numerical inputs are perfectly valid in classification tasks.

Fourth, if we were limited to only categorical inputs, many practical problems would be less solvable or would require heavy encoding, but the broader field and common practice embrace mixed or numerical inputs as well.

In summary, the claim is inaccurate because classification problems do not require inputs to be exclusively categorical; they can use numerical, categorical, or a combination of features to predict a categorical output.

MS MS Machine Learning (Georgios A Lentzas) SP2025 Quiz 1

Classification problems involve using only categorical inputs to predict a categorical output.

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