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Supervised learning involves training a model using labeled datasets where the algorithm learns to map inputs to the correct output labels. Once trained, the model can predict labels for new, unseen data. It is widely used for classification and regression problems in machine learning. Why Other Options are Wrong: b) Clustering data without labels is unsupervised learning, not supervised learning. c) Continuous learning from feedback describes reinforcement learning. d) Interaction with an environment also refers to reinforcement learning. e) Time series prediction is a specific application of supervised learning but does not define it comprehensively.
Statements: Some pins are needles.
All needles are ropes.
Some ropes are buckets.
All buckets are trees.
Conclusions:I. some...
Three statements are given followed by three conclusions numbered I, II and III. Assuming the statements to be true, even if they seem to be at variance...
Statements:
Only BB are LL.
Only a few BB are DD.
No BB is AA.
A few AA are OO.
Conclusions:
I. A...
Statements: Some nails are hairs.
...
Statements: Some guitars are harmoniums.
No harmonium is a sitar.
Conclusions: I. Some guitars are not sitars.
II. Some sitars ar...
Conclusions:
A few platforms may be trains.
Some tickets being passenger is a possibility.
Statements:
Some plastics are not elastics. Some rubbers are not fibres. Some elastics are not fibres.
Statements: Some winters are summers.
All summers are springs.
All springs are seasons.
Statements:
Only a few Red are Green.
No Green is Yellow.
No Pink is Red.
Conclusions:
I. Some Red are not Yellow.
Statements: No cow is a bull.
No bull is a goat.
Some g...