The next two questions refer to the following chart. The chart below has a single point for every breed of dog. Each point indicates the maximum weight of that breed and the maximum number of years that breed of dog lives to. Which of the following conclusions is BEST supported by the scatter plot? Max Weight vs. Max Lifespan of Dog Breeds 20 Max Life Span 10 50 100 150 200 Max Weight A. Dog breeds with a higher maximum weight tend to have shorter maximum lifespans B. Dog breeds with a higher maximum weight tend to have longer maximum lifespans C. All dog breeds tend to have the same average lifespan D. Older dogs weigh more than younger dogs 0000
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- Computer Science Consider the following data: X 2 4 1 5 0 8 8.5 2 4.5 10 Y 3 4 2.5 6 1 6 7 2 5.5 10 • Use linear regression and calculate the regression coefficients and obtain the line of regression for this data. Show your steps. • Draw the data points and the line you estimated using linear regression. • Compute the Least Squares Line Fit ERROR for the line you find. • Do you think that we can find another regression method to get better regression results and less error? Justify you answer. NOT: without coding.Assuming a total sample of 1079 persons, among which 520 persons are having autism and 559 are healthy persons. When we pass the data of 520 autism patients into the KNN classifier, it correctly predicted “220” patients as autism category and the remaining patients into healthy category. Similarly, from 559 healthy persons, the KNN categorize “100” as autism patients and the remaining as healthy persons. In the above scenario, if “autism” is considered as “positive class” and “healthy person” is considered as negative class then find the: True Positive True Negative False Positive False Negative Sensitivity (True positive rate) Specificity (True negative rate) Accuracy PrecisionAssuming a total sample of 1079 persons, among which 520 persons are having autism and 559 are healthy persons. When we pass the data of 520 autism patients into the KNN classifier, it correctly predicted “220” patients as autism category and the remaining patients into healthy category. Similarly, from 559 healthy persons, the KNN categorize “100” as autism patients and the remaining as healthy persons. In the above scenario, if “autism” is considered as “positive class” and “healthy person” is considered as negative class then find the: True Positive True Negative False Positive False Negative Sensitivity (True positive rate) Specificity (True negative rate) Accuracy Precision Draw the confusion matrix Assuming a total sample of 1079 persons, among which 520 persons are having autism and 559 are healthy persons. When we pass the data of 520 autism patients into the KNN classifier, it correctly predicted “220” patients as autism category and the…
- Please help with the artificial intelligence question below thanks! Given a number of data samples (X, Class) in the attached file where each data sample consists of a variable X and a Class whose value is 1 or 2. (1) Using the given sample data, use the Gradient Descent algorithm to predict the logistic regression model (Note: the logistic regression model is NOT a regression model). (2) Using the logistic regression model as a solution to point (1) above, predict the Class of a sample that has a value of X = 5.6Estimate John’s weight if you gave: A) The degree of membership for each fuzzy fact: • John is tall (degree 0.5). • John is of medium height (degree 0.7). • John is short (degree 0.1) • John is well-built (degree 0.3). • John is weak (degree 0.2). Where fuzzy membership functions are defined in terms of numerical values of an underlying crisp attribute. (For example: Short, Medium and Tall in terms of height. Weak and Well-built in terms of muscle mass)Say that you have the following initial settings for binary logistic regression: x = [1, 1, 3] w = [0, -2, 0.75] b = 0.5 2. Given that x's label is 1, what is the value of w_1, w_2, and w_3 at time t + 1 if the learning rate is 1? For this problem, you may ignore the issue of updating the bias term. 3. What is the value of P(y = 1 | x) given your updated weights from the previous question? 4. Given that x's label is 1, what is the value of the bias term at time t + 1 if the learning rate is 1? 5. What is the value of P(y = 1 | x) given both your updated weights and your updated bias term? 6. Given that x's label is 0, what is the value of P(y = 0| x) at time t + 1 if the learning rate is 0.1? Round your answer to the nearest 1000th as a number [0, 1].
- A police academy has just brought in a batch of new recruits. All recruits are given an aptitude test and a fitness test. Suppose a researcher wants to know if there is a significant difference in aptitude scores based on a recruits’ fitness level. Recruits are ranked on a scale of 1 – 3 for fitness (1 = lowest fitness category, 3 = highest fitness category). Using “ApScore” as your dependent variable and “FitGroup” as your independent variable, conduct a One-Way, Between Subjects, ANOVA, at α = 0.05, to see if there is a significant difference on the aptitude test between fitness groups. Identify the correct values for dfbetween and dfwithin A. dfbetween = 7 dfwithin = 7 B. dfbetween = 13 dfwithin = 2 C. dfbetween = 2 dfwithin = 13 D. dfbetween = 2 dfwithin = 2Based on the following box-plots of the fitness (objective) values obtained after running 8 search algorithms on a given instance for a minimisation problem for 30 trials, indicate whether each of the five given statements below is TRUE or FALSE and provide your reasoning Statements: (i) A3 is a hill climbing algorithm (ii) The best performing algorithm is A7 on average for the instance (iii) The best solution in a single trial is achieved by the algorithm A6 for the instance (iv) The algorithm A3 performs better than A4 and this performance difference is statistically significant for the instance (v) The algorithm A6 performs slightly better than A4 on average for the instancePlease help with the ai question below, thanks Given a number of data samples (X, Class) in the attached file where each data sample consists of a variable X and a Class whose value is 1 or 2. (1) Using the given sample data, use the Gradient Descent algorithm to predict the logistic regression model (Note: the logistic regression model is NOT a regression model). (2) Using the logistic regression model as a solution to point (1) above, predict the Class of a sample that has a value of X = 5.6