In machine learning, what input the model needs to train itself and predict the unknown?

Question

Options

• AManual procedure
• BNeural Networks
• CTraining algorithm
• Dhistorical data

• A
  14% (2)
• B
  7% (1)
• C
  7% (1)
• D
  71% (10)

Explanation

Historical data is correct because machine learning models learn patterns by processing examples of past inputs and known outcomes - this is what allows them to generalize and make predictions on new, unseen data. Without historical data, a model has nothing to learn from.

A (Manual procedure) is wrong because machine learning is defined by its ability to learn automatically from data, not from step-by-step human-written rules. C (Training algorithm) is the method used to process the data, not the input itself - it is the mechanism, not the raw material. B (Neural Networks) is an architecture type, a structural choice for building models, and has nothing to do with what is fed into a model as input.

Memory tip: Think of a model like a student preparing for an exam - it studies past examples (historical data) to answer future questions it has never seen before.

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