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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- 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?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,…
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- 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/37anOxiHow does the "slice and dice" method work for the multiple model?Please show step-by-step explanations. Thank you.
- You are working on a spam classification system using regularized logistic regression. "Spam" is a positive class (y = 1)and "not spam" is the negative class (y=0). You have trained your classifier and there are m= 1000 examples in the cross-validation set. The chart of predicted class vs. actual class is: Predicted class: 1 Predicted class: 0 Actual class: 1 85 15 For reference: Accuracy = (true positives + true negatives)/(total examples) Precision = (true positives)/(true positives + false positives) Recall = (true positives)/ (true positives + false negatives) F1 score = (2* precision * recall)/(precision + recall) What is the classifier's F1 score (as a value from 0 to 1)? Write all steps Use the editor to format your answer Actual class: 0 890 10The following is true about sensitivity: Group of answer choices a) The output of the model is said to be inversely sensitive if the output of the model changes a small amount for a large change in an input variable b) Sensitivity is not an important concept in modeling c) It can help the modeler tell, on a relative basis, what are the important variables d) A variable is considered NOT very sensitive if a small change in the variable results `in a large change in the output of the model.Question 48. Let us return to the Titanic data set. We now have learned several models and want to choose the best one. We used three different methods to validate these models: The training error rate (apparent error rate), the error rate on an external test set and the error rate estimated by a 10-fold cross validation. Training Error | Error on the test set | Cross Validation Error 0.18 Learner Decision Tree 0.22 0.21 Random Forest 0.01 0.10 0.12 1-Nearest-Neighbour 0.18 0.19 Which of the following statements are correct? a) 1-Nearest-Neighbour has a perfect training error and hence it should be used here. b) Random Forests outperforms both 1-Nearest-Neighbour and the Decision Tree in terms of prediction error. c) Not just in this case, but in general, Cross Validation is the better validation strategy and should always be preferred over the error on a single test set. d) Not just in this case, but in general, Decision Trees always perform worse than Random Forests.