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The most likely concern with a one-feature, machine-learning model is high error due to:
Suggested Answer: A
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A model with only one feature is unlikely to capture the true complexity of the data's underlying relationships, leading to systematic underfitting - i.e., high bias.
12 comments · newest first
LO
Lottie
2 months ago
I agree with A) bias. It simplifies too much, missing important patterns.
EL
Elly
2 months ago
I lean towards C) variance. Too much focus on one feature can lead to overfitting.
PE
Penney
2 months ago
I think it's A) bias. One feature can't capture complexity.
CR
Cristina
2 months ago
I agree with Tom, variance plays a huge role in errors.
TA
Tayna
2 months ago
Not so sure about that, could be B) dimensionality too.
VA
Valentin
2 months ago
Surprised this is even a question, it's clearly A) bias!
BE
Bettina
3 months ago
I think it's more about C) variance, honestly.
PA
Paris
3 months ago
Definitely A) bias, that's a common issue.
TA
Tanesha
3 months ago
I lean towards A) bias too, especially since one feature might not capture the complexity of the data.
VI
Vincent
3 months ago
This feels similar to a practice question we did on model evaluation. I think it could be B) dimensionality, but I'm not confident.
FE
Felicidad
3 months ago
I'm not entirely sure, but I remember something about variance being a concern in more complex models.
LU
Lucia
4 months ago
I think the answer might be A) bias, since a one-feature model could oversimplify the problem.