conference paper
ipython --pdb pipeline.py ./DS2_clean.csv
Results will show on the screen like the following:
NaiveBayes with Tfidf
GaussianNB(priors=None, var_smoothing=1e-09)
precision recall f1-score support
0 0.31 0.95 0.47 1234
1 0.98 0.59 0.73 6201
accuracy 0.65 7435
macro avg 0.65 0.77 0.60 7435
weighted avg 0.87 0.65 0.69 7435
-----------------------------------
NaiveBayes with Word2Vec-TFIDF
GaussianNB(priors=None, var_smoothing=1e-09)
precision recall f1-score support
0 0.26 0.72 0.38 1234
1 0.92 0.59 0.72 6201
accuracy 0.61 7435
macro avg 0.59 0.66 0.55 7435
weighted avg 0.81 0.61 0.66 7435
-----------------------------------
NaiveBayes with Word2Vec
GaussianNB(priors=None, var_smoothing=1e-09)
precision recall f1-score support
0 0.65 0.83 0.73 1234
1 0.96 0.91 0.94 6201
accuracy 0.90 7435
macro avg 0.81 0.87 0.83 7435
weighted avg 0.91 0.90 0.90 7435
-----------------------------------
DecisionTree with Tfidf
DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
max_depth=None, max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=5,
min_weight_fraction_leaf=0.0, presort='deprecated',
random_state=None, splitter='best')
precision recall f1-score support
0 0.77 0.83 0.80 1234
1 0.97 0.95 0.96 6201
accuracy 0.93 7435
macro avg 0.87 0.89 0.88 7435
weighted avg 0.93 0.93 0.93 7435
-----------------------------------
DecisionTree with Word2Vec-TFIDF
DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
max_depth=None, max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort='deprecated',
random_state=None, splitter='best')
precision recall f1-score support
0 0.30 0.33 0.31 1234
1 0.86 0.84 0.85 6201
accuracy 0.76 7435
macro avg 0.58 0.59 0.58 7435
weighted avg 0.77 0.76 0.76 7435
-----------------------------------
DecisionTree with Word2Vec
DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
max_depth=None, max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, presort='deprecated',
random_state=None, splitter='best')
precision recall f1-score support
0 0.63 0.65 0.64 1234
1 0.93 0.92 0.93 6201
accuracy 0.88 7435
macro avg 0.78 0.79 0.78 7435
weighted avg 0.88 0.88 0.88 7435
-----------------------------------
NaiveBayes with FastText
GaussianNB(priors=None, var_smoothing=1e-09)
precision recall f1-score support
0 0.55 0.73 0.63 1234
1 0.94 0.88 0.91 6201
accuracy 0.86 7435
macro avg 0.75 0.81 0.77 7435
weighted avg 0.88 0.86 0.87 7435
-----------------------------------
RandomForest with Tfidf
RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_jobs=None, oob_score=False, random_state=None,
verbose=0, warm_start=False)
precision recall f1-score support
0 0.80 0.87 0.83 1234
1 0.97 0.96 0.96 6201
accuracy 0.94 7435
macro avg 0.89 0.91 0.90 7435
weighted avg 0.94 0.94 0.94 7435
-----------------------------------
AdaBoost with Tfidf
AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,
n_estimators=50, random_state=None)
precision recall f1-score support
0 0.76 0.95 0.85 1213
1 0.99 0.94 0.97 6222
accuracy 0.94 7435
macro avg 0.88 0.95 0.91 7435
weighted avg 0.95 0.94 0.95 7435
-----------------------------------
SVC with Word2Vec
SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
precision recall f1-score support
0 0.80 0.77 0.79 1234
1 0.96 0.96 0.96 6201
accuracy 0.93 7435
macro avg 0.88 0.87 0.87 7435
weighted avg 0.93 0.93 0.93 7435
-----------------------------------
RandomForest with Word2Vec-TFIDF
RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_jobs=None, oob_score=False, random_state=None,
verbose=0, warm_start=False)
precision recall f1-score support
0 0.63 0.11 0.19 1234
1 0.85 0.99 0.91 6201
accuracy 0.84 7435
macro avg 0.74 0.55 0.55 7435
weighted avg 0.81 0.84 0.79 7435
-----------------------------------
RandomForest with Word2Vec
RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_jobs=None, oob_score=False, random_state=None,
verbose=0, warm_start=False)
precision recall f1-score support
0 0.83 0.64 0.72 1234
1 0.93 0.97 0.95 6201
accuracy 0.92 7435
macro avg 0.88 0.81 0.84 7435
weighted avg 0.92 0.92 0.91 7435
-----------------------------------
AdaBoost with Word2Vec
AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,
n_estimators=50, random_state=None)
precision recall f1-score support
0 0.76 0.68 0.72 1213
1 0.94 0.96 0.95 6222
accuracy 0.91 7435
macro avg 0.85 0.82 0.83 7435
weighted avg 0.91 0.91 0.91 7435
-----------------------------------
AdaBoost with Word2Vec-TFIDF
AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,
n_estimators=50, random_state=None)
precision recall f1-score support
0 0.47 0.15 0.23 1213
1 0.85 0.97 0.91 6222
accuracy 0.83 7435
macro avg 0.66 0.56 0.57 7435
weighted avg 0.79 0.83 0.80 7435
-----------------------------------
SVC with Word2Vec-TFIDF
SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
precision recall f1-score support
0 0.67 0.00 0.00 1234
1 0.83 1.00 0.91 6201
accuracy 0.83 7435
macro avg 0.75 0.50 0.46 7435
weighted avg 0.81 0.83 0.76 7435
-----------------------------------
DecisionTree with FastText
DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',
max_depth=None, max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=5,
min_weight_fraction_leaf=0.0, presort='deprecated',
random_state=None, splitter='best')
precision recall f1-score support
0 0.51 0.53 0.52 1234
1 0.91 0.90 0.90 6201
accuracy 0.84 7435
macro avg 0.71 0.71 0.71 7435
weighted avg 0.84 0.84 0.84 7435
-----------------------------------
SVC with FastText
SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
precision recall f1-score support
0 0.79 0.67 0.73 1234
1 0.94 0.96 0.95 6201
accuracy 0.92 7435
macro avg 0.86 0.82 0.84 7435
weighted avg 0.91 0.92 0.91 7435
-----------------------------------
AdaBoost with FastText
AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,
n_estimators=50, random_state=None)
precision recall f1-score support
0 0.74 0.60 0.66 1253
1 0.92 0.96 0.94 6182
accuracy 0.90 7435
macro avg 0.83 0.78 0.80 7435
weighted avg 0.89 0.90 0.89 7435
-----------------------------------
RandomForest with FastText
RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,
criterion='gini', max_depth=None, max_features='auto',
max_leaf_nodes=None, max_samples=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_jobs=None, oob_score=False, random_state=None,
verbose=0, warm_start=False)
precision recall f1-score support
0 0.85 0.49 0.62 1234
1 0.91 0.98 0.94 6201
accuracy 0.90 7435
macro avg 0.88 0.74 0.78 7435
weighted avg 0.90 0.90 0.89 7435
-----------------------------------
MLPClassifier with Word2Vec
MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=100, learning_rate='constant',
learning_rate_init=0.001, max_fun=15000, max_iter=200,
momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,
power_t=0.5, random_state=None, shuffle=True, solver='adam',
tol=0.0001, validation_fraction=0.1, verbose=False,
warm_start=False)
precision recall f1-score support
0 0.82 0.72 0.77 1213
1 0.95 0.97 0.96 6222
accuracy 0.93 7435
macro avg 0.88 0.85 0.86 7435
weighted avg 0.93 0.93 0.93 7435
-----------------------------------
MLPClassifier with Word2Vec-TFIDF
MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=100, learning_rate='constant',
learning_rate_init=0.001, max_fun=15000, max_iter=200,
momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,
power_t=0.5, random_state=None, shuffle=True, solver='adam',
tol=0.0001, validation_fraction=0.1, verbose=False,
warm_start=False)
precision recall f1-score support
0 0.53 0.33 0.41 1213
1 0.88 0.94 0.91 6222
accuracy 0.84 7435
macro avg 0.70 0.64 0.66 7435
weighted avg 0.82 0.84 0.83 7435
-----------------------------------
MLPClassifier with FastText
MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=100, learning_rate='constant',
learning_rate_init=0.001, max_fun=15000, max_iter=200,
momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,
power_t=0.5, random_state=None, shuffle=True, solver='adam',
tol=0.0001, validation_fraction=0.1, verbose=False,
warm_start=False)
precision recall f1-score support
0 0.78 0.73 0.75 1246
1 0.95 0.96 0.95 6189
accuracy 0.92 7435
macro avg 0.86 0.84 0.85 7435
weighted avg 0.92 0.92 0.92 7435
-----------------------------------
SVC with Tfidf
SVC(C=1.0, break_ties=False, cache_size=200, class_weight=None, coef0=0.0,
decision_function_shape='ovr', degree=3, gamma='scale', kernel='linear',
max_iter=-1, probability=False, random_state=None, shrinking=True,
tol=0.001, verbose=False)
precision recall f1-score support
0 0.80 0.88 0.84 1234
1 0.98 0.96 0.97 6201
accuracy 0.94 7435
macro avg 0.89 0.92 0.90 7435
weighted avg 0.95 0.94 0.94 7435
-----------------------------------
MLPClassifier with Tfidf
MLPClassifier(activation='relu', alpha=0.0001, batch_size='auto', beta_1=0.9,
beta_2=0.999, early_stopping=False, epsilon=1e-08,
hidden_layer_sizes=100, learning_rate='constant',
learning_rate_init=0.001, max_fun=15000, max_iter=200,
momentum=0.9, n_iter_no_change=10, nesterovs_momentum=True,
power_t=0.5, random_state=None, shuffle=True, solver='adam',
tol=0.0001, validation_fraction=0.1, verbose=False,
warm_start=False)
precision recall f1-score support
0 0.82 0.82 0.82 1246
1 0.96 0.96 0.96 6189
accuracy 0.94 7435
macro avg 0.89 0.89 0.89 7435
weighted avg 0.94 0.94 0.94 7435
-----------------------------------
GradientBoosting with Tfidf
GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=5,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=None, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False)
precision recall f1-score support
0 0.84 0.59 0.69 1234
1 0.92 0.98 0.95 6201
accuracy 0.91 7435
macro avg 0.88 0.78 0.82 7435
weighted avg 0.91 0.91 0.91 7435
-----------------------------------
GradientBoosting with Word2Vec
GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=2,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=None, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False)
precision recall f1-score support
0 0.81 0.70 0.75 1234
1 0.94 0.97 0.95 6201
accuracy 0.92 7435
macro avg 0.87 0.83 0.85 7435
weighted avg 0.92 0.92 0.92 7435
-----------------------------------
GradientBoosting with Word2Vec-TFIDF
GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=5,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=None, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False)
precision recall f1-score support
0 0.61 0.11 0.18 1234
1 0.85 0.99 0.91 6201
accuracy 0.84 7435
macro avg 0.73 0.55 0.55 7435
weighted avg 0.81 0.84 0.79 7435
-----------------------------------
GradientBoosting with FastText
GradientBoostingClassifier(ccp_alpha=0.0, criterion='friedman_mse', init=None,
learning_rate=0.1, loss='deviance', max_depth=3,
max_features=None, max_leaf_nodes=None,
min_impurity_decrease=0.0, min_impurity_split=None,
min_samples_leaf=1, min_samples_split=5,
min_weight_fraction_leaf=0.0, n_estimators=100,
n_iter_no_change=None, presort='deprecated',
random_state=None, subsample=1.0, tol=0.0001,
validation_fraction=0.1, verbose=0,
warm_start=False)
precision recall f1-score support
0 0.80 0.55 0.65 1234
1 0.92 0.97 0.94 6201
accuracy 0.90 7435
macro avg 0.86 0.76 0.80 7435
weighted avg 0.90 0.90 0.89 7435
-----------------------------------
- Elmo
- Word2Vec
- TFIDF
- Word2vec+TFIDF
- FastText
- BERT
- ULM-Fit
- GloVe
- SVM (kernel: linear)
- Perceptron
- Decision Tree
- AdaBoost
- Random Forest
- Gradient Boosting
- Gusassion NB
- Multinomial NB