Refers to the scenario in which the analyst builds a model that does a great job of explaining the sample of data on which it is based but fails to accurately predict outside the sample data. a. oversampling b. underfitting c. overfitting d. undersampling

c. Overfitting

Step-by-step explanation:

When building a model, the model learns from the data on which it is trained. But, for the model, its accuracy in predicting or explaining the data it was trained is not enough.

Real accuracy of the model can be evaluated when the model faces previously unseen data. The models which are successful on predicting or explaining the training data, but performing significantly less accurate and lacking successfull prediction on new data is said overfitted on training data.

To overcome this phenomenon, some regularization methods can be used Additionally, feature processing like scaling, normalization,removing outliers may help to avoid overfitting.

the camry sales+accord sale=828,000. to make it so the two numbers are 18 thousand apart and still equal 828,000, you first need to find out what half of 828,000 is. 828,000/2=414,000. for the two numbers to equal 828,000, you have to add the same amount as you subtract from the middle. in this case, the number is 9,000 because it's half of 18,000. to equal 828,000, you have to add 9,000 to 414,000 for the camry and subtract 9,000 from 414,000 for the accord. 414,000+9,000=423,000.

414,000-9,000=405,000.

423,000+405,000=828,000.

423,000-405,000=18,

hope it

207/8 is the right