Naive Bayes is known as a bad estimator and its probability outputs are not taken too seriously. Describe the limitations of the assumptions of the Naive Bayes model. Describe an approach to the construction and how to use the Naïve Bayes model for Automatic classification of emails in folders
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Naive Bayes is known as a bad estimator and its probability outputs are not taken too seriously. Describe the limitations of the assumptions of the Naive Bayes model. Describe an approach to the construction and how to use the Naïve Bayes model for Automatic classification of emails in folders
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- Which of the following statements about confusion matrix is wrong A) Confusion matrix is a performance measure for probability prediction techniques B) Confusion matrix is derived based on classification rules with cut-off value 0.5 C) Confusion matrix is derived based on training partition to measure a model’s predictive performance D) None of the above I need typed answer with explanation KConsider values shown in the table below:i=1 (cold) i=2 (allergy) i=3 (stomach pain) p(Hi)0.60.30.1 p(E1 |Hi)0.30.80.3 p(E2 |Hi)0.60.90.0Those values represent (hypothetically) three mutually exclusive and exhaustive hypotheses for the patient’s condition. For example, H1: the patient has a cold, H2: the patient has an allergy, and H3: the patient has stomach pain with their prior probabilities, p(Hi)’s and two conditionally independent pieces of evidence (E1, patient sneezes and E2, patient coughs) which support these hypotheses to differing degrees. Therefore;a) Compute the posterior probabilities for the hypothesis if the patient sneezes. What is the conclusion that can be derived from this condition?b) Based on the answer from the previous result, as the patient coughs are now observed, compute the posterior probabilities for this condition. Explain the results.Draw the probability tree for the situation. Draw the reverse tree for the situation.
- Design a set of states, rewards, and rules for an intelligent agent playing a simple board game. You may choose any board game that you are familiar with. In your design, be sure to consider the following: States, including starting and ending states, and possible actions Rewards or penalties for reaching a state Rules for navigating from one state to another Compare your approach to the Markov Decision Process (MDP) that you learned about in this module. What similarities and differences do you see between your approach and the MDP?Question 1: 1) What is the difference between a Stochastic problem and a Deter ministic problem? 2)Explain how a phonetic alphabet can be regarded as an error-correcting code for spoken word. Here is 2 sub questions attempt only if u can do both otherwise skip Type answer required Thank youIt is important to clarify the link between NPV and risk. In a variety of high-risk situations, you may provide support for your assertions by using various algorithms.
- Hi guys can you check the given problem below and provide me with a solution and also the steps showing how you arrive at the answers. Thank you. Using fuzzy logic to predict college GPA A college admissions director wants to use High School GPA and ACT score to predict the final GPA when a student graduates from her institution. She divides GPAs into 5 fuzzy sets: Poor – student is at risk for being admitted because of risky HS GPA Marginal – student is marginally qualified due to his HS GPA Average – student has average HS GPA Good – student has a good HS GPA Very Good – student has a very good HS GPA She will use the same fuzzy sets for College GPAs and HS GPAs shown below. Fuzzy Sets for HS and College GPA Diagram here is showing in the image below From left, the fuzzy sets are: Poor. Poor intersects x axis when x = 1, and x=2. The apex is at (1.5,1) Marginal: Marginal intersects the x axis at x = 1.5 and x = 2.5. The apex is at (2,1.0) Fair: Fair intersects the x axis at 2,…Markoviian Decision Processes Identify a real-world problem not used as an example in class => cannot use Pacman, racing cars, FPS, Blackjack…. and then describe this problem as a MDP noting all required components (states, actions, policy, etc.) Give an exampleAlgorithm for VerifyNashInputs: Finite-state deterministic joint policy π, specifications φj for all j, Nashfactor , precision δ, failure probability p.Outputs: True or False along with a set of punishment strategies τ .
- Good evening. Can you check the problem below and provide me with a solution and steps and formula on how you arrived at the answers. Using fuzzy logic to predict college GPA A college admissions director wants to use High School GPA and ACT score to predict the final GPA when a student graduates from her institution. She divides GPAs into 5 fuzzy sets: Poor – student is at risk for being admitted because of risky HS GPA Marginal – student is marginally qualified due to his HS GPA Average – student has average HS GPA Good – student has a good HS GPA Very Good – student has a very good HS GPA She will use the same fuzzy sets for College GPAs and HS GPAs shown below. Fuzzy Sets for HS and College GPA Diagram here showing in the fig 1 image below From left, the fuzzy sets are: Poor. Poor intersects x axis when x = 1, and x=2. The apex is at (1.5,1) Marginal: Marginal intersects the x axis at x = 1.5 and x = 2.5. The apex is at (2,1.0) Fair: Fair intersects the x axis at 2, and 3..…An artificial intelligence system was design to forecast the financial trading market and predict change based on observed variables from the business environment. A machine learning model is to be designed in understand and forecast trends based on Markov chain. The variables to be observed are • Population(A) • Poverty rate(B) • Inflation (C) Available resources(D) Theses variables can affect each other or impact upon one another. P. R2 PI P1 P2 P/ Pis Pus P- P1 P2 PA Ps Ps2 The matix P represent the general form of an state matrix. Below is a table of the variables with their effect on each other. А B с D A 0.1 0.1 0.1 0.7 B 0.2 0.3 0.2 0.3 с 0.3 0.4 0.1 0.2 D 0.4 0.2 0.2 0.2 Answer the following question below: a. Provide a general markov state diagram for the variable described. b. Assuming the below factor, A, affected the state in the table above, produce the state diagram for, A, and calculate the final state. 25 20 25 30 .20 30 25 30 A= 25 20.40.10 30 30.10.30 c. Discuss the…Amortized analysis demines the average running time of the worst case True False