Eleven million seven hundred sixty thousand and eight hundred twenty five
Answer:
Bias is the difference between the average prediction of our model and the correct value which we are trying to predict and variance is the variability of model prediction for a given data p[oint or a value which tells us the spread of our data the variance perform very well on training data but has high error rates on test data on the other hand if our model has small training sets then it's going to have smaller variance & & high bias and its contribute more to the overall error than bias. If our model is too simple and has very few parameters then it may have high bias and low variable. As the model go this is conceptually trivial and is much simpler than what people commonly envision when they think of modelling but it helps us to clearly illustrate the difference bewteen bias & variance.
Answer:
a < 3.5
Step-by-step explanation:
2a+3 <10
subtract 3 from each side
2a +3 -3 < 10-3
2a < 7
divide by 2
a < 7/2
a < 3.5
Answer:
8.10+0.22
Is 8.33
Step-by-step explanation:
Answer:
It should be the first answer.
Step-by-step explanation:
The first answer says "If Nemo is a fish, then he swims". That makes sense. Then the second one says "If Nemo does swim, then he is a fish". That would make sense, but if my name was Nemo, I can swim. That doesn't mean I'm a fish. The the third one says "If Nemo is not a fish, then he doesn't swim". That doesn't make sense. Say my name is Nemo. I'm not a fish. I can swim though!
