Problem 3 - Support Vector Machines Use the Iris trainging set Explore the data to find the best two features to use We are mostly doing this so we can visualize the results Split the data set into 80% training and 20% testing Create a SVM to model the data Create a visualization that shows the line and the margins Create anonther visualization that shows the decision surface Do not include the test data points Randomly select 10 test points and add them to the visualization. Color them based on their label Are the random test points consistently on the correct side of the line? Predict the label for ALL of the test data Show a confusion matrix Calculate the F1 measure Grading criteria: SVM graphically appears to correctly to use a reasonable line F1 measure is consistent with what we showed in class ### You code here Explanation of your model:
Problem 3 - Support Vector Machines Use the Iris trainging set Explore the data to find the best two features to use We are mostly doing this so we can visualize the results Split the data set into 80% training and 20% testing Create a SVM to model the data Create a visualization that shows the line and the margins Create anonther visualization that shows the decision surface Do not include the test data points Randomly select 10 test points and add them to the visualization. Color them based on their label Are the random test points consistently on the correct side of the line? Predict the label for ALL of the test data Show a confusion matrix Calculate the F1 measure Grading criteria: SVM graphically appears to correctly to use a reasonable line F1 measure is consistent with what we showed in class ### You code here Explanation of your model:
Chapter6: Managing Multiple Worksheets And Workbooks
Section: Chapter Questions
Problem 16RA
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Problem 2 - K-means
- Use the Ecoli dataset at https://archive.ics.uci.edu/ml/datasets/Ecoli
- Ignore the label and create clusters using k values between 4 and 6.
- Pick the best k value and explain why you picked it
- Show any calculations you used to pick the best cluster
- Create two visualization
- One colors the nodes with the cluster membership
- The other colors the nodes based on the actual label
- Grading criteria: Adequately describe how to pick the best cluster and successful create the required visualizations
### Your code here
Provide an explanation of your model:
Problem 3 - Support Vector Machines
- Use the Iris trainging set
- Explore the data to find the best two features to use
- We are mostly doing this so we can visualize the results
- Split the data set into 80% training and 20% testing
- Create a SVM to model the data
- Create a visualization that shows the line and the margins
- Create anonther visualization that shows the decision surface
- Do not include the test data points
- Randomly select 10 test points and add them to the visualization. Color them based on their label
- Are the random test points consistently on the correct side of the line?
- Predict the label for ALL of the test data
- Show a confusion matrix
- Calculate the F1 measure
- Grading criteria:
- SVM graphically appears to correctly to use a reasonable line
- F1 measure is consistent with what we showed in class
### You code here
Explanation of your model:
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