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Machine learning projects with r
Machine learning projects with r
Machine learning projects with r
Machine learning projects with r
Machine learning projects with r
Machine learning projects with r
Machine learning projects with r
Machine learning projects with r
Machine learning projects with r
Machine learning projects with r
Machine learning projects with r
Machine learning projects with r
Machine learning projects with r
Machine learning projects with r
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Machine learning projects with r

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Introduction of two machine learning practice using R

Introduction of two machine learning practice using R

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  1. MACHINE LEARNINGPROJECTS WITH RYiou (Leo) Li
  2. Outline Classification of glass data Clustering of glass data
  3. Classification by ridge regression3
  4. Plotting the three classes by four features4 Simple Scatterplot Matrix 11 12 13 14 15 0.5 1.0 1.5 2.0 1.525 V2 1.515 15 14 13 V3 12 11 4 3 V4 2 1 0 2.0 1.5 V5 1.0 0.5 1.515 1.525 0 1 2 3 4
  5. Performance looks good when consider only the classification error rate5
  6. Performance is poor when consider ROC6
  7. Using high order polynomial helps improve ROC7 Decision point
  8. Using high order polynomial helps improve TPR and FPR!8 Y ~ [V2, V3, …, V10, V2*V3, V2*V4, …] Training Test True Positive Rate 0.6820833 0.55 False Positive Rate 0.008368031 0.0804762 Error rate 0.03953965 0.1270588 Y ~ [V2, V3 … , V10] Training Test True Positive Rate 0 0 False Positive Rate 0.00685288 0.007142857 Error rate 0.1104277 0.1102941
  9. Notes on ridge regression9 1. The ridge solutions are not invariant under scaling of the inputs --- usually standardize the input --- so that the solution is invariant to scaling of inputs 2. Intercept β0 should be left out of the penalty term! --- so that the solution is invariant to the choice of origin of inputs and outputs
  10. Outline Classification of glass data Clustering of glass data
  11. Multi-Dimensional Scaling of glassdata (Labeled as: 1,2,3,5,6,7) Metric MDS 6 1 2 3 5 6 4 7 Coordinate 2 2 0 -2 -4 -2 0 2 4 6 Coordinate 1
  12. Kmeans of glass K-means cluster 1.0 0.8 0.6 Correct rate 0.4 0.2 0.0 Original labels
  13. Hierarchical of glass Hierachical cluster 1.0 0.8 0.6 Correct rate 0.4 0.2 0.0 Original labels
  14. Correct rate 0.0 0.2 0.4 0.6 0.8 1.0 EM of glass EMOriginal labels

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