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:
y = 3/7x + 51
Step-by-step explanation:
y = 3/7x + 11 is parallel to the line so it means that both of their gradient are same so:
y = mx + c
m= gradient point = (-21 , 42)
= 3/7
now we sub the m and the point into the formula which is y= mx+c because we should find the c first then we can find the equation of the line:
42 = 3/7 (-21) + c
42 = -9 + c
42 + 9 = c
51 = c
c = 51
now you hv to rewrite again the equation become y= 3/7x + 51
So now your final answer is : y = 3/7x +51
Y=0.5x-0.5
( u find the slope using rise/run then after finding the slope u use the equation and a point on the graph to find the y-intercept
A) y= -2, 3, 7
B) y= 8, 0, -6
C) y= -8, 1, 7
d) y= 1, -2, -6
4n = 3m - 1
4n = 3m - 1
Subtract 3m from both sides.
-3m + 4n = -1
Subtract 4n from both sides.
-3m = -4n - 1
Divide both sides by -3.
m = 4/3n + 1/3
m = 4/3n + 1/3
