Bresenham line drawing algorithm: For Slope |m|>1: Either value of y is increased OR both x and y is increased using decision parameter.

esc A Question 14 of 20: Select the best answer for the question. 14. Use Gauss-Jordan elimination to solve the following linear system: -3x + 4y = -6 5x - y = 10 O A. (2,0) O B. (2,-5) OC. (-6,2) O D. (2,2) O Mark for review (Will be highlighted on the review page) > ← 21 W = S #3 E D لم G $ 4 { R بر 19 F % 50 F ف Oll GY O U *00 C 8 A

webwork/mcb1007_int_prob stat / mcb1007-hmwrk3 / 4 MCB1007-Hmwrk3: Problem 4 Previous Problem Problem List Next Problem (2 points) The joint probability mass function of X and Y is given by p(1,1)= 0.4 Note: You can earn partial credit on this problem. p(2, 1) = 0.1 p(3, 1) = 0.1 (a) Compute the conditional mass function of Y given X = 3: P(Y = 1|X = 3) = P(Y=2|X = 3) = P(Y = 3|X = 3) = (b) Are X and Y independent? (enter YES or NO) Preview My Answers Submit Answers You have attempted this problem 0 times. You have 10 attempts remaining. p(1, 2) = 0.1 p(2, 2) = 0.05 p(3, 2) = 0.1 Email WeBWork TA p(1,3)= 0.05 p(2,3) = 0.05 p(3,3) = 0.05

A rectangle has a perimeter of 20. If the area of the rectangle is to be maximized, the length of each side should be 5

Se x + learn.edgenuity.com/player/ nciples Semester A This algorithm is using a heuristic Mark this and return brute force approach heuristic tractable problem superpolynomial time O to approximate the best route. ↑ A mapping application cannot consider every factor to calculate the fastest route in a reasonable amount of time. Instead, the application uses an algorithm that considers only the most important factors such as total distance and traffic congestion along popular routes. MD A Save and Exit Next G < English Sign out ☆ * TIME REMAINING 58:34 Submit Mar 27 19 0 Kinley Heath X ⠀ + 10:25 0 4

: |: Section 3 - Random Forests Decision Trees on their own are effectiev classifiers. The true power, though, comes from a forest of trees -- multiple decision trees working together as an ensemble learner. We create multiple trees, each using a subset of the available attributes, let them each make a guess at the correct classification, and then take the majority vote as the predicted class. Sounds tricky, right? Once again, sklearn to the rescue. We are going to see if a random forest can improve on our decision tree accuracy for the bank data. To begin, we are going to create a Random Forest using the wheat training and test data from the previous section. First, reload our data. 1 # reload the wheat dataset from UCI 2 3 df = pd. read_csv ("seeds_dataset.txt", sep='\\t', engine='python') 4 6 7 df.columns = ['a', 'p', 'compactness', 'length', 'width', 'coeff', 'length_g', 'type'] print (f'Our data has {df.shape [0]} rows and {df.shape [1]} columns') 8 #Mark 70% of the data for…

Part 3 You are part of a team responsible for running a successful video streaming service with millions of views daily. For marketing and research reasons, you have been asked to implement an algorithm that efficiently finds the k most viewed videos daily. You should expect k<

Rod cutting problem: A cutting factory buys long steel rods and then and cuts them into shorter rods, which it then sells. Each cut is free. The selling price is based on the length of the cut. The factory want to determine the best cuts in order to maximize its revenue. Below, the price that the factory can make for each length of rod is given. Determine the maximum revenue, rn, obtainable by cutting up the rod and selling the pieces using bottom-up dynamic-programming approach. Length, I 1 2 3 4 5 6 7 Price, pi 2 4 5 6 9 10 12   r5 = ?