classifier_stats.log

## Done training
# Stats for LogisticRegression
## Best params: {'vect__ngram_range': (1, 1), 'tfidf__smooth_idf': False, 'tfidf__sublinear_tf': True, 'vect__preprocessor': <function no_usernames at 0xa1540d4>, 'tfidf__use_idf': True, 'vect__stop_words': None, 'clf__penalty': 'l1', 'clf__C': 2.0} 
## Best Score: 0.68442947358 
             precision    recall  f1-score   support

 "negative"       0.51      0.24      0.33       384
"objective"       0.67      0.84      0.75      1326
 "positive"       0.73      0.62      0.67       968

avg / total       0.67      0.68      0.66      2678

[[  94  221   69]
 [  53 1119  154]
 [  38  333  597]]
###############################################
# Done testing LogisticRegression 
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## Done training
# Stats for LinearSVC
## Best params: {'vect__ngram_range': (1, 1), 'tfidf__smooth_idf': True, 'tfidf__sublinear_tf': True, 'vect__preprocessor': <function placeholders at 0xa15417c>, 'tfidf__use_idf': False, 'vect__stop_words': None, 'clf__C': 0.5} 
## Best Score: 0.66152405057 
             precision    recall  f1-score   support

 "negative"       0.49      0.26      0.34       384
"objective"       0.67      0.81      0.73      1326
 "positive"       0.67      0.62      0.64       968

avg / total       0.65      0.66      0.64      2678

[[  98  195   91]
 [  54 1071  201]
 [  46  324  598]]
###############################################
# Done testing LinearSVC 
###############################################

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# Finished Stats:
[{'clf': LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, penalty='l2', random_state=None, tol=0.0001), 'best_score': 0.68442947357973238}, {'clf': LinearSVC(C=1.0, class_weight=None, dual=True, fit_intercept=True,
     intercept_scaling=1, loss='l2', multi_class='ovr', penalty='l2',
     random_state=None, tol=0.0001, verbose=0), 'best_score': 0.66152405057009223}]
###############################################
###############################################
# Best algorithm LogisticRegression, with best score of 0.68442947358 #
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