Confusion Matrix, 4x4 Assigned Class A C Class of Origin A 0.58 0.14 0.15 0.13 В 0.02 0.74 0.20 0.04 0.06 0.18 0.70 0.06 D 0.03 0.04 0.07 0.86
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Please Answer the questions below with reference to the confusion matrix shown:
- A test sample assigned to which class is the one we accept with highest confidence? Justify your answer please.
- Suppose that you have a binary classifier for each pair of classes that has 95% accuracy. Which two classes would you target?
- For (b), what is the final accuracy of the reduced (3x3) confusion matrix combined with a high-accuracy binary (95%) classifier for these two targeted classes? How does this compare to the original accuracy?
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- 1. Answer the questions:a. Draw logistic regression flow chartb. Write pseudo code of logistic regressionc. Explain each step of flow chart and pseudo coded. First design the question (choose x1 and x2 values for each class (blue and red)or initialize weight and bias),then find proper weight and bias values of greenline that can be seen in Figure 1. This line should separate two classes.Answer the following questions for the method intersection() depicted in the attached image. Does the partition “Validity of s1” satisfy the completenessproperty? If not, give a value for s1 that does not fit in anyblock.Does the partition “Validity of s1” satisfy the disjointnessproperty? If not, give a value for s1 that fits in more than oneblock.Does the partition “Relation between s1 and s2” satisfy thecompleteness property? If not, give a pair of values for s1 ands2 that does not fit in any block.Does the partition “Relation between s1 and s2” satisfy thedisjointness property? If not, give a pair of values for s1 and s2that fits in more than one block.If the “Base Choice” criterion were applied to the two partitions(exactly as written), how many test requirements would result?Please solve this question using Matlab and attach the simulation with the answer.
- For the following confusion matrix, what is the weighted accuracy if the weight for the Cat class is 0.2, for the Dog class it is 0.5, and for the Perico class it is 0.3? Cat Parakeet Dog (Predicted) (Predicted) (Predicted) Cat (Real) 14 Dog (Real) 18 2 Parakeet (Real) 13 7 20 2.A group of international experts in fuzzy set theory were invited to classify the overall performance of two research scholars, after a lot of discussion they agreed to consider the following attributes(Creativity, presentation skills) in the classification of the scholar .design a fuzzy inference system to find how would they judge similarity of the research scholars with varying attributes ?Explain the procedure in details. Creativity Skills 1 2 3 4 5 Presentation skills 1 No creativity Bad 2 Fair Fair 3 Good Good 4 Very good 5 Excellent Very good Excellent Creativity 1 5 4 3 2 1 2 4 4 3 2 1 3 4 3 3 2 1 + 3 2 2 1 1 5 3 2 1 1 1 B- Draw the Genetic algorithm flowchart and write the code for each step.Let's revisit our first problem, where we want to set up a series of chess matches so we can rank six players in our class. As we did before, we will assume that everyone keeps their chess rating a private secret; however, when two players have a chess match, the person with the higher rating wins 100% of the time. But this time, we are only interested in identifying the BEST of these six players and the WORST of these six players. (We don't care about the relative ordering or ranking of the middle four players.) Your goal is to devise a comparison-based algorithm that is guaranteed to identify the player with the highest rating and the player with the lowest rating. Because you are very strong at Algorithm Design, you know how to do this in the most efficient way. Here are five statements. A. There exists an algorithm to solve this problem using 6 matches, but there does not exist an algorithm using only 5 matches. B. There exists an algorithm to solve this problem using 7 matches,…
- Please provide steps to answer this question: Your task is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck. Implement and train a classification model for the Titanic dataset (the dataset can be found here: https://www.kaggle.com/c/titanic). Please ignore the test set (i.e., test.csv) and consider the given train set (i.e., train.csv) as the dataset. What you need to do: 1. Data cleansing 2. Split the dataset (i.e., train.csv) into a training set (80% samples) and a testing set (20% samples) 3. Train your model (see details below) 4. Report the overall classification accuracies on the training and testing sets 5. Report the precision, recall, and F-measure scores on the testing setI have a 70000x785 dataset (70000 instances and 785 features) consisting of 10 different types of classes. I need to find a selection algorithm for finding the optimal training set size N as detailed in the question below. What is an example of a selection algorithm that can do this?You build a model predicting blood pressure as a function of three variables: weight (numeric) age (numeric) income (categorical: low, medium, high) You first specify your model as: blood pressure ~ age * income + weight How many parameters (k) does your model have? (Remember, we do not count the grand mean in k) You change the above model specification to be: blood pressure ~ age * income + weight * income How many parameters does your model have now? You change your model to include the three-way interaction (which, remember, includes all two-way interactions and main effects, too!) Your model now looks like this: blood pressure ~ age * income * weight How many parameters does your model have now?
- Effective JUnit testing means, at least in part, thinking about as many edge cases as possible. True FalseInstructions: In the next exercise, you will use a known dataset used as an example for classification problems. Refer to: Index of /ml/machine-learning-databases/balance-scale (uci.edu). The problem has 3 classes and 4 input features. The possible classes are (B: balanced, L: left, R: right), and the 4 input features are: (LW: left weight, RW: right weight, LD: left distance, RD: Right distance). The balance shows up the value ‘B’ when LW + LD = RD + RW. Each raw of the file data is constructed as the following: Class, LW, LD, RW, RD. Example: B, 3, 2, 4, 1. In addition, all the input features might have values 1, ..., 5. For more information refer to website given before. Remark: you are free to propose a preprocessing of the input features, also you’re advised to try different values of the learning rate and observe the training curve. Remark: in your code, you are asked to use python, numpy and matplotlib. You shouldn’t use libraries like scikit, tensorflow, pytorch, or any other…Your task is simple: use machine learning to create a model that predicts which passengers survived the Titanic shipwreck. Implement and train a classification model for the Titanic dataset (the dataset can be found here: https://www.kaggle.com/c/titanic). Please ignore the test set (i.e., test.csv) and consider the given train set (i.e., train.csv) as the dataset. What you need to do: 1. Data cleansing 2. Split the dataset (i.e., train.csv) into a training set (80% samples) and a testing set (20% samples) 3. Train your model (see details below) 4. Report the overall classification accuracies on the training and testing sets 5. Report the precision, recall, and F-measure scores on the testing set 1. Required Model (100 pts): Implement and train a logistic regression as your classification model. • You have to use Sklearn deep learning library. • You may want to refer to this tutorial: https://bit.ly/37anOxi