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Introduction of two machine learning practice using R

Introduction of two machine learning practice using R

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- MACHINE LEARNINGPROJECTS WITH RYiou (Leo) Li
- Outline Classification of glass data Clustering of glass data
- Classification by ridge regression3
- 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
- Performance looks good when consider only the classification error rate5
- Performance is poor when consider ROC6
- Using high order polynomial helps improve ROC7 Decision point
- 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
- 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
- Outline Classification of glass data Clustering of glass data
- 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
- Kmeans of glass K-means cluster 1.0 0.8 0.6 Correct rate 0.4 0.2 0.0 Original labels
- Hierarchical of glass Hierachical cluster 1.0 0.8 0.6 Correct rate 0.4 0.2 0.0 Original labels
- Correct rate 0.0 0.2 0.4 0.6 0.8 1.0 EM of glass EMOriginal labels

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