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Which machine learning technique does not produce a model that is a generalization of its input data (instead retains the individual inputs)?

If a machine learning model were predicting coronavirus infections and correctly diagnosed 50 patients with coronavirus, incorrectly diagnosed 22 patients with coronavirus, correctly declined to diagnose 500 patients with coronavirus, and missed diagnosing 200 patients with coronavirus when those patients were indeed ill with the disease, what is the precision of this model in this testing scenario?  

If a k-means algorithm was classifying the datapoint (2,2) and currently had cluster centroids (1,0), (2,4), and (4, 5) for clusters 1, 2, and 3 respectively, which cluster would the datapoint (2,2) be assigned to, assuming euclidean distance?

Planning is finding a sequence of to take from an initial state to arrive at a goal state.

In the context of an ontology, which of the following would be a “class”?

In the context of an ontology, what would be appropriate for the “domain” of a property “birthPlace”?

Consider the following datalog knowledge base:imm_west(r101,r103)imm_west(r103,r105)    imm_west(r105,r107)imm_east(E,W)←imm_west(W,E)next_door(E,W)←imm_east(E,W)next_door(W,E)←imm_west(W,E)Given this knowledge base, which of the following propositional queries would yield a ‘no’ answer?

Giving credit to your sources allows you to

A patient with a Foley catheter inserted 3 days ago needs a urine sample for culture and sensitivity. What is the appropriate action for the nurse to take?

Where do you collect the urine specimen from on a patient with an indwelling catheter on day two?