Evaluating algorithms (1 mark)Complete this sentence. When evaluating algorithms, training and test sets usually:[Fill in the blank]

Question

BlackTom AI · Accepted Answer

In this question, we’re asked to complete the sentence about evaluating algorithms and how training and test sets relate to each other. 
Option A: 'are the same data sets.' If we used the same data for training and testing, we would risk evaluating the model on data it has already seen, which could inflate performance and fail to indicate true generalization. This is generally avoided in proper evaluation practices. 
Option B: 'are drawn from very different sources.' While one might sometimes use different sources, the standard practice for a clean evaluation is not about different sources per se, but about ensuring independence between training and test observations, not arbitrary source differences. This makes this option incomplete or misleading as a general statement. 
Option C: 'are carefully selected to stress algorithms.' Stress-testing can be a goal in some benchmarking setups, but the usual educational or standard ML evaluation uses separation for independence rather than adversarially stressing the algorithm. This description overspecifies the intent and type of separation typically used in evaluation. 
Option D: 'are taken from the same set of data, but are non-overlapping.' This captures the conventional approach: splitting the data into training and test (and sometimes validation) subsets drawn from the same overall dataset, with no overlap between training and test instances to maintain independent evaluation. The non-overlapping constraint preserves the integrity of the test results while using the same data origin. 
Collectively, these explanations highlight why the standard answer emphasizes non-overlapping partitions drawn from the same dataset, ensuring that evaluation reflects generalization rather than memorization.

Evaluating algorithms (1 mark)Complete this sentence. When evaluating algorithms, training and test sets usually:[Fill in the blank]单项选择题

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