166
I guess you just subtract the numbers
297-131=
Or there is something am I missing on here
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.
Sometimes system outputs are limited because the amount of the necessary information that is perceived in the system .
We can create a equation to find out.
since we are finding a number where when multiplied by 1.25(this is the decimal form of the percentage) gives us 25, we know that the number we are finding is less than 25. here is the equation.
1.25(x)=25
divide both sides by 1.25 to get "x" alone.
x= 20
