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  1. from sklearn.dummy import DummyClassifier
  2. dummy_model = DummyClassifier(strategy = 'most_frequent', random_state = 0)
  3. dummy_model.fit(x_train, y_train)
  4.    
  5. [[9700    0    0]
  6.  
  7.  [ 211    0    0]
  8.  
  9.  [  82    0    0]]
  10.    
  11. precision    recall  f1-score   support
  12.  
  13.       1       0.97      1.00      0.99      9700
  14.       2       0.00      0.00      0.00       211
  15.       3       0.00      0.00      0.00        82
  16.  
  17. avg / total       0.94      0.97      0.96      9993
  18.    
  19. from sklearn.linear_model import LogisticRegression
  20. logit_model = LogisticRegression(C=0.05, random_state=18, class_weight='balanced', penalty='l1')
  21. logit_model.fit(x_train, y_train)
  22.    
  23. [[9700    0    0]
  24.  
  25.  [ 211    0    0]
  26.  
  27.  [  82    0    0]]
  28.    
  29. precision    recall  f1-score   support
  30.  
  31.       1       0.97      1.00      0.99      9700
  32.       2       0.00      0.00      0.00       211
  33.       3       0.00      0.00      0.00        82
  34.  
  35. avg / total       0.94      0.97      0.96      9993
  36.    
  37. logit_model_base = LogisticRegression(random_state = 18)
  38. from sklearn.model_selection import GridSearchCV
  39. parameters = {'C': [0.03, 0.05, 0.08, 0.1, 0.3, 0.5, 10], 'penalty': ['l1', 'l2']}
  40. logit_model_best = GridSearchCV(logit_model_base, param_grid = parameters, cv = 3)
  41. logit_model_best.fit(x_train, y_train)
  42.    
  43. [[9700    0    0]
  44.  
  45.  [ 211    0    0]
  46.  
  47.  [  82    0    0]]
  48.    
  49. precision    recall  f1-score   support
  50.  
  51.       1       0.97      1.00      0.99      9700
  52.       2       0.00      0.00      0.00       211
  53.       3       0.00      0.00      0.00        82
  54.  
  55. avg / total       0.94      0.97      0.96      9993
  56.    
  57. from sklearn.linear_model import LogisticRegressionCV
  58. logit_model_cv = LogisticRegressionCV(cv = 10, class_weight = 'balanced')
  59. logit_model_cv.fit(x_train, y_train)
  60.    
  61. [[2831 3384 3485]
  62.  
  63.  [  36  104   71]
  64.  
  65.  [   9   28   45]]
  66.    
  67. precision    recall  f1-score   support
  68.  
  69.       1       0.98      0.29      0.45      9700
  70.       2       0.03      0.49      0.06       211
  71.       3       0.01      0.55      0.02        82
  72.  
  73. avg / total       0.96      0.30      0.44      9993
  74.    
  75. from sklearn.linear_model import LogisticRegressionCV
  76. logit_model_cv = LogisticRegressionCV(cv = 10)
  77. logit_model_cv.fit(x_train, y_train)
  78.    
  79. [[9700    0    0]
  80.  
  81.  [ 211    0    0]
  82.  
  83.  [  82    0    0]]
  84.    
  85. precision    recall  f1-score   support
  86.  
  87.       1       0.97      1.00      0.99      9700
  88.       2       0.00      0.00      0.00       211
  89.       3       0.00      0.00      0.00        82
  90.  
  91. avg / total       0.94      0.97      0.96      9993
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