DEA-1TT5 · Question #319
What kind of ML explicitly requires splitting the data into training and testing datasets before running an algorithm against that data?
The correct answer is D. Supervised Learning. Depending on the type of ML approach and the phase of the buildout, labels or tags might be another essential component to data collection. In supervised learning approaches, clearly tagged data and direct feedback ensures that the algorithm can self-learn.
Question
What kind of ML explicitly requires splitting the data into training and testing datasets before running an algorithm against that data?
Options
- ADeep Learning
- BUnsupervised Learning
- CReinforcement Learning
- DSupervised Learning
How the community answered
(60 responses)- A5% (3)
- B3% (2)
- C13% (8)
- D78% (47)
Explanation
Depending on the type of ML approach and the phase of the buildout, labels or tags might be another essential component to data collection. In supervised learning approaches, clearly tagged data and direct feedback ensures that the algorithm can self-learn.
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