Problem 2 - K-meansUse the Ecoli dataset at https://archive.ics.uci.edu/ml/datasets/EcoliIgnore the label and create clusters using k values between 4 and 6.Pick the best k value and explain why you picked itShow any calculations you used to pick the best clusterCreate two visualizationOne colors the nodes with the cluster membershipThe other colors the nodes based on the actual labelGrading criteria: Adequately describe how to pick the best cluster and successful create the required visualizations### Your code hereProvide an explanation of your model: Problem 3 - Support Vector Machines Use the Iris trainging setExplore the data to find the best two features to useWe are mostly doing this so we can visualize the resultsSplit the data set into 80% training and 20% testingCreate a SVM to model the dataCreate a visualization that shows the line and the marginsCreate anonther visualization that shows the decision surfaceDo not include the test data pointsRandomly select 10 test points and add them to the visualization. Color them based on their labelAre the random test points consistently on the correct side of the line?Predict the label for ALL of the test dataShow a confusion matrixCalculate the F1 measureGrading criteria:SVM graphically appears to correctly to use a reasonable lineF1 measure is consistent with what we showed in class### You code here Explanation of your model:

Support vector machine which refers to the another simple algorithm that it is the every machine…

Answered: Problem 3 - Support Vector Machines Use…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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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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