EE559_Project

• 2-class problem

• Dataset (# data pts.)

  • training: 184
    • Class 0: 69 (37.5%)
    • Class 1: 115 (62.5%)
  • test: 60

• Sequential Backward Selection

  • Most contributing features:
  • ISI > Rain > DMC > FFMC > DC > RH > BUI > Ws > Temperature

• Required reference systems

  • Trivial system
    python3 trivial.py

    • Test F1-score: 0.5
    • Test Accuracy: 0.5

  • Baseline system
    python3 baseline.py

    • Drop "Date"
    • Test F1-score: 0.6286
    • Test Accuracy: 0.7833

• Technique 1: Perceptron Learning (Drop "Date")
  python3 perceptron.py --M 4 --epoch 200 --plot_title perceptron (M-fold cross-validation)

  • Val F1-score: 0.9113
  • Val Accuracy: 0.9076
  • Test F1-score: 0.8846
  • Test Accuracy: 0.9

  python3 perceptron.py --M 4 --epoch 200 --normalization --plot_title p_norm

  • Apply min-max normalization to all features
  • Val F1-score: 0.9405
  • Val Accuracy: 0.9457
  • Test F1-score: 0.8679
  • Test Accuracy: 0.8833

  python3 perceptron.py --M 4 --epoch 200 --standardization --plot_title p_std

  • Apply standardization to all features
  • Val F1-score: 0.9368
  • Val Accuracy: 0.9457
  • Test F1-score: 0.92
  • Test Accuracy: 0.93

  python3 perceptron.py --M 4 --epoch 200 --standardization --feat_reduction --plot_title p_feat_reduct

  • Four least contributing features: Temperature -> Ws -> BUI -> RH
  • Drop (1,2,3,4) features
  • Val F1-score: (0.9725, 0.9805, 0.9763, 0.9875)
  • Val Accuracy: (0.9674, 0.9728, 0.9728, 0.9837)
  • Test F1-score: (0.9583, 0.9787, 0.9388, 0.9583)
  • Test Accuracy: (0.9667, 0.9833, 0.95, 0.9667)

  python3 perceptron.py --standardization --feat_reduction --extra_feat --plot_title p_add_1_feat

  • Val F1-score: 0.9882
  • Val Accuracy: 0.9783
  • Test F1-score: 0.9787
  • Test Accuracy: 0.9833

• Technique 2: KNN Classifier (Drop "Date", with Standardization)
  python3 kNN.py --M 4 --k 7 --plot_title kNN

  • The following results are for k = (2, 3, 4, 5, 6, 7, 8)
  • Val F1-score: (0.8287, 0.8532, 0.8605, 0.8745, 0.886, 0.8678, 0.8739)
  • Val Accuracy: (0.8478, 0.8641, 0.8696, 0.875, 0.875, 0.8641, 0.8804)
  • Test F1-score: (0.7, 0.8444, 0.8095, 0.8182, 0.8372, 0.8444, 0.8182)
  • Test Accuracy: (0.8, 0.8833, 0.8667, 0.8667, 0.8833, 0.8833, 0.8667)

  python3 kNN.py --M 4 --k 7 --feat_reduction --plot_title kNN_feat_reduct

  • Four least contributing features: Temperature -> Ws -> BUI -> RH
  • Drop (1,2,3,4) features
  • Val F1-score: (0.8898, 0.9108, 0.9236, 0.9313)
  • Val Accuracy: (0.8967, 0.9076, 0.9293, 0.9239)
  • Test F1-score: (0.8444, 0.8182, 0.8444, 0.8182)
  • Test Accuracy: (0.8833, 0.8667, 0.8833, 0.8667)

  python3 kNN.py --extra_feat --feat_reduction --plot_title kNN_add_1_feat

  • Drop temperature
  • Val F1-score: 0.9767
  • Val Accuracy: 0.9565
  • Test F1-score: 0.8085
  • Test Accuracy: 0.85

  python3 kNN.py --extra_feat --plot_title kNN_add_1_feat

  • Val F1-score: 0.9767
  • Val Accuracy: 0.9565
  • Test F1-score: 0.8511
  • Test Accuracy: 0.8833

• Technique 3: MSE Classifier (Drop "Date")
  python3 MSE.py --plot_title MSE (b=1 for all data pts)

  • Val F1-score: 0.79
  • Val Accuracy: 0.8424
  • Test F1-score: 0.7568
  • Test Accuracy: 0.85

• Technique 4: SVM (Drop "Date")
  python3 SVM.py --Linear SVM

  • Train F1_score= 0.8771929824561403
  • Train Accuracy= 0.8478260869565217
  • Test F1_score= 0.8571428571428571
  • Test Accuracy= 0.8666666666666667

  python3 SVM.py --RBF SVM -Test RBF Accuracy= 0.8333333333333334 -Test Linear Accuracy= 0.85 -Test RBF F1_score= 0.8214285714285715 -Test Linear F1_score= 0.8363636363636363

  python3 SVM.ipynb --feat_reduction --plot_title

  • Four least contributing features: Temperature -> Ws -> BUI -> RH
  • Drop (1,2,3,4) features
  • Test F1-score RBF: (0.8214, 0.8214, 0.8214, 0.8070)
  • Test Accuracy RBF: (0.8333, 0.8333, 0.8333, 0.8166)
  • Test F1-score Linear: (0.83636, 0.83636, 0.83636, 0.8214)
  • Test Accuracy Linear: (0.85, 0.85, 0.85, 0.8333)

• Technique 5: Logistic Regression (Drop "Date")
  python3 Logistic Regression.ipynb

  • Train Logistic regression F1_score= 0.9385964912280702
  • Train Logistic regression Accuracy= 0.9239130434782609
  • Test Logistic regression F1_score= 0.8260869565217391
  • Test Logistic regression Accuracy= 0.8666666666666667

  python3 Logistic Regression.ipynb --feat_reduction only ISI

  • Best contributing features: ISI
  • Accuracy for train for Only ISI: 0.9130434782608695
  • Accuracy for test only ISI: 0.8666666666666667

  python3 Logistic Regression.ipynb --feat_reduction --plot_title

  • Three least contributing features: Temperature -> Ws -> RH
  • Drop (1,2,3) features
  • Accuracy for train 1drop : 0.923913043478260
  • Accuracy for test 1drop: 0.9
  • Accuracy for train 2drop : 0.9239130434782609
  • Accuracy for test 2drop: 0.8833333333333333
  • Accuracy for train 3drop : 0.9293478260869565
  • Accuracy for test 3drop: 0.8666666666666667