Q.1.7 Discuss the features of a good test case
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Q.1.7 Discuss the features of a good test case.
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- k Means Clustering In the context of healthcare research or business, provide two scenarios where you'd use an un-supervised machine learning model and two more scenarios where you'd use a supervised machine learning mode. You can use real scenarios or make something that seems plausible. |Add the missing pieces from checkpoint B while using this codeImagine that the final exams for five courses need to be sched- uled, and only two time slots are available. There are plenty of classrooms, but no student can take two exams simultaneously. The classlists for the five course are • Classlist for course 1: ci {Akiko,Erin,Sandy} • Classlist for course 2: c2 {Carl, Dan, Wilma} • Classlist for course 3: c3 {Akiko,Bart,Terry} • Classlist for course 4: C4 {Erin, Dan, Val} • Classlist for course 5: c5 {Carl, Bart,José}
- Need a implementation plan for this : Our aim is to take increase toy paratrooper flight time with the parachutes provided and find suitable alternatives to reduce its descent through the most feasible means. The justification for increased flight time being if we were to take these model paratroopers to represent actual paratroopers' lower descent speeds will provide safer landings and more maneuverable descents. We learned through observation that our paratrooper's initial descent is practically freefall and extremely erratic which gives us our main point of optimization. Our only variables which we can alter are wind resistance provided by said parachute, elevation, conditions(temperature) within a margin, and weight. We will make alterations where it is possible to reach our goal of a practical lower descent speed and thereby an increase in descent time and more stable, safe flight for our toy paratroopers.3. Consider one-dimensional totalistic rules with 3 colors and two neigh- bors as before. White (0), grey (1) and black (2) are the colors. Totalistic means that the state in the middle cell in the next generation only depends on the sum of the 3 cells. This sum goes from 0 to 6. How many rules of this type? You have to modify the command CellularAutomaton a little. Read about the Details. Time to have a look at a special rule, rule 1329. Since you have 3 colors you must express this number in base 3. Use command BaseForm. Start with one grey cell in the middle of a long string of white cells and try to understand what is going on. Is the pattern periodic?Question 2 Consider the correctness statement x == x { x = x + 1; } P where x has type integer. The correctness statement is valid when P is the assertion: Ox>0 Ox 1 Ox< 1 false The correctness statement is invalid in all above cases.
- Match the metrics with their corresponding definition. TPR/Recall PPV/Precision TNR/Specificity Accuracy [Choose ] The ratio of correctly classified predictions over all predictions The ratio of the positive predictions that were correct over the total number of positive predictions made. The ratio over the number of negative predictions that were correct over all the true negative class samples. The ratio over the number of positive predictions that were correct over all the true positive class samples [Choose ]Please written by computer source Consider a classification problem where we wish to determine if a human subject is likely to have a heart attack in the next year. We use four features - x1 (Age), x2 (hospHistory), x3 (FavoriteFood), and x4 (Gender). Each feature takes on one of a discrete number of values, shown below: Age: Child Teen Adult SeniorCitizen hospHistory Never Recent DecadesAgo FavoriteFood Apple, Steak Pasta Ice Cream Gender: Male Female We wish to classify each user as either yi=LikelyAttack or yi=NotLikelyAttack. 1. How can the features above be transformed to use a logistic classifier? For each feature, use a transformation that reasonably captures the structure of the data while minimizing the number of parameters to learn. 2. How many parameters are required to learn a separating hyper-plane (w and any other necessary elements) for logistic classification with the features converted in question 1? (Work from your answer to question 1. If you could…This one.
- please provide orignal solution.3. Big-O Dating Game. Match each data structure with the correct desired behavior so that each algorithm achieves its best Big-O (runtime efficiency). Feel free to justify each match. Structure A: I'm the kind of algorithm who wants a data structure that isn't slow minded. When I ask it a question, I expect my data structure to be able to find the answer quickly and efficiently, but I don’t need to keep my data in any particular order. (What data structure would be appropriate in a find-heavy algorithm?) Structure B (2): I like to keep things tidy. I like it best when everything is in its rightful place and would like to meet a data structure that isn't going to make that difficult. (What data structure would be appropriate for an algorithm that needs to maintain the ordering of my data?)PROBLEM IN IN PYTHON 3. THIS IS A CODING PROBLEM. NOT A MULTIPLE CHOICE PROBLEM. ANSWER SHOULD BE IN PYTHON. IF YOU DO NOT KNOW HOW TO SOLVE AND THINK IT IS A MULTIPLE CHOICE PROBLEM, DO NOT ANSWER. class Solution: """ RAW_TRADE_HEADER = ["trade_id", "trade_date", "time_of_trade", "portfolio", "exchange", "product", "product_type", "expiry_dt", "qty", "strike_price", "side"] """ def process_raw_trade(self, raw_trade: List): def run(self) -> List[Tuple[str, str]]:Can you kindly please give me an example of a Python code that plots the efficient frontier curve of asset classes, whereby for a given range of risk levels (x-axis of efficient frontier), the curve must show the 1) portfolio weights, 2) the portfolio return and 3) Sharpe ratio for a given risk free rate. NB: The code must be in Python (Programming Language) and the code does not have to be complete. I simply just need guidance on how to go about plotting the efficient frontier that shows the above 3 things.