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) it converges
B) 1998.047
C) 2000
d) The sum is getting smaller and smaller
Step-by-step explanation:
Formula: a_n+a_1*r^(r-1)
(1000*((0.5^10)-1))/(0.5-1)=1998.047
(1000*((0.5^30)-1))/(0.5-1)=2000
Step-by-step explanation:
38.43 - 1.29x
Variable x represents the number of songs purchased
Not sure why this question didn't save the answer I just wrote (I'm new to the app)
Answer:
you can use similar triangle to make known degrees in problems to make then easier to solve. With similar triangle, the angles are the same, but the scale is different. So by using this, one can solve both at the same time, and just just scale up the smaller one or scale down the larger, by the given/found scale.
Step-by-step explanation:
