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MATLAB Assessment Task

Part 1:

Run Logistic Regression and calculate accuracy on the test set based on the model you trained on the train set Calculate principal components and Run Logistic Regression again based on ALL PCs (principal components) Hint: we have 9 features, therefore 9 PCs should be calculated for each record in the train and test dataset Question 1: How many PCAs explain more than 90% of variation?

Question 2: How much variation is explained based on two first PCs? Based on your answers to the above questions, choose the number of necessary PCs that explain more than 90% of variation Run Logistic Regression once more and calculate accuracy again

Question 3: Which of these three models performs better? ( (a)Logistic Regression, or

(b)All PCs and Logistic Regression, or

(c)PCs which explain more than 90% and Logistic Regression)

Part 2: Run K-means clustering on ALL the nine features and set K=2.

Question 4: How well the dataset is clustered into two groups based on K-means by comparing cluster and class labels. Hint: There are two classes and two clusters. You need to decide cluster 1 is representing class 1 and cluster 2 is representing class 2 or cluster 1 is representing class 2 and cluster 2 is representing class 1 based on majority of class labels in each cluster, and count the accuracy of k-means (based on the number of correct samples in each cluster).

Reporting this accuracy will show how well the k-means worked without knowing the labels beforehand. Run K-means once more based on the first two PCs and visualize the results on a graph by using two colors for data points in cluster 1 and cluster

2. Create another graph by visualizing based on two first PCs and use two colors for class 1 and class 2 based on the actual labels we have.

Question 5: Compare the results side by side and describe how well the K-means algorithm performs aer using two PCs. Hint: In the following figures, you can see the iris dataset based on two first PCs. In the first figure, the colors are representing class labels. In the second figure they are representing the clusters. By comparing two figures, you can see kmeans could cluster one of the classes correctly and mixed the other two.

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  • Uploaded By : Keith
  • Posted on : December 04th, 2018

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