Answer:
Training a model using labeled data and using this model to predict the labels for new data is known as: <u>Supervised Learning.</u>
Explanation:
Supervised learning is a set of techniques that allows future predictions based on behaviors or characteristics analyzed in labeled historical data. A label is nothing more than the output that the data set has returned for historical data, already known. In supervised learning, it assumes that we start from a previously labeled data set, that is, we know the value of the target attribute for the data set that we have.
Answer: A
Explanation: Shallow Copy
if the field to be copied is a primitive type, then the value is copied more if the field to be copied is a memory address (or an object itself), or the address is copied. Thus, if the address is changed by an object, the change will be reflected everywhere.
Deep Copy the data is copied in both situations. This approach is more loaded and slower.
<span>If my memory serves me
right, the correct answer to this question is “</span>Representativeness”.
<span>Representativeness is one of the heuristics used when
using to form judgments of others. In this case, when one or a small sample of
extrovert speaks about of their goals and their jobs, we assume it to be
similar to other extroverts. This then shows that they represent others. </span>
There isn't an endless supply of all resources because there are only limited resources.
This is true. However, I need 9-10 hours of sleep to function properly. Please mark Brainliest!!!