ru
| classifiers \ vectorizers | bag of words | fast text | word to vector |
|---|---|---|---|
| naive bayes | 98.91598915989161 | 77.23577235772358 | 75.70009033423668 |
| random forest | 95.5736224028907 | 92.32158988256549 | 94.39927732610658 |
| support vector machine | 95.39295392953929 | 92.8635953026197 | 94.67028003613369 |
en
| classifiers \ vectorizers | bag of words | fast text | word to vector |
|---|---|---|---|
| naive bayes | 99.20983318700614 | 80.33362598770852 | 76.82177348551362 |
| random forest | 97.27831431079895 | 93.94205443371378 | 96.57594381035996 |
| support vector machine | 96.22475856014047 | 94.5566286215979 | 96.7515364354697 |
More details report
fitting the model by lang=ru
bag_of_words
accuracy of MultinomialNB-> 98.91598915989161
precision recall f1-score support
0 1.00 0.99 0.99 838
1 0.96 0.99 0.98 269
accuracy 0.99 1107
macro avg 0.98 0.99 0.99 1107
weighted avg 0.99 0.99 0.99 1107
accuracy of RandomForestClassifier-> 95.5736224028907
precision recall f1-score support
0 0.95 1.00 0.97 838
1 0.99 0.83 0.90 269
accuracy 0.96 1107
macro avg 0.97 0.91 0.94 1107
weighted avg 0.96 0.96 0.95 1107
accuracy of SVC-> 95.39295392953929
precision recall f1-score support
0 0.95 1.00 0.97 838
1 0.99 0.82 0.90 269
accuracy 0.95 1107
macro avg 0.97 0.91 0.93 1107
weighted avg 0.96 0.95 0.95 1107
fast_text
accuracy of MultinomialNB-> 77.23577235772358
precision recall f1-score support
0 0.77 1.00 0.87 838
1 0.95 0.07 0.12 269
accuracy 0.77 1107
macro avg 0.86 0.53 0.50 1107
weighted avg 0.81 0.77 0.69 1107
accuracy of RandomForestClassifier-> 92.32158988256549
precision recall f1-score support
0 0.93 0.97 0.95 838
1 0.90 0.77 0.83 269
accuracy 0.92 1107
macro avg 0.91 0.87 0.89 1107
weighted avg 0.92 0.92 0.92 1107
accuracy of SVC-> 92.8635953026197
precision recall f1-score support
0 0.94 0.97 0.95 838
1 0.89 0.81 0.85 269
accuracy 0.93 1107
macro avg 0.91 0.89 0.90 1107
weighted avg 0.93 0.93 0.93 1107
word2vec
accuracy of MultinomialNB-> 75.70009033423668
/home/drukhary/.cache/pypoetry/virtualenvs/experimental-Hok3v7Pw-py3.11/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
/home/drukhary/.cache/pypoetry/virtualenvs/experimental-Hok3v7Pw-py3.11/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
/home/drukhary/.cache/pypoetry/virtualenvs/experimental-Hok3v7Pw-py3.11/lib/python3.11/site-packages/sklearn/metrics/_classification.py:1344: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.
_warn_prf(average, modifier, msg_start, len(result))
precision recall f1-score support
0 0.76 1.00 0.86 838
1 0.00 0.00 0.00 269
accuracy 0.76 1107
macro avg 0.38 0.50 0.43 1107
weighted avg 0.57 0.76 0.65 1107
accuracy of RandomForestClassifier-> 94.39927732610658
precision recall f1-score support
0 0.95 0.98 0.96 838
1 0.94 0.83 0.88 269
accuracy 0.94 1107
macro avg 0.94 0.90 0.92 1107
weighted avg 0.94 0.94 0.94 1107
accuracy of SVC-> 94.67028003613369
precision recall f1-score support
0 0.96 0.97 0.96 838
1 0.90 0.88 0.89 269
accuracy 0.95 1107
macro avg 0.93 0.92 0.93 1107
weighted avg 0.95 0.95 0.95 1107
fitting the model by lang=en
bag_of_words
accuracy of MultinomialNB-> 99.20983318700614
precision recall f1-score support
0 1.00 0.99 0.99 870
1 0.97 1.00 0.98 269
accuracy 0.99 1139
macro avg 0.98 0.99 0.99 1139
weighted avg 0.99 0.99 0.99 1139
accuracy of RandomForestClassifier-> 97.27831431079895
precision recall f1-score support
0 0.97 1.00 0.98 870
1 1.00 0.88 0.94 269
accuracy 0.97 1139
macro avg 0.98 0.94 0.96 1139
weighted avg 0.97 0.97 0.97 1139
accuracy of SVC-> 96.22475856014047
precision recall f1-score support
0 0.96 0.99 0.98 870
1 0.98 0.86 0.91 269
accuracy 0.96 1139
macro avg 0.97 0.93 0.95 1139
weighted avg 0.96 0.96 0.96 1139
fast_text
accuracy of MultinomialNB-> 80.33362598770852
precision recall f1-score support
0 0.80 0.99 0.88 870
1 0.86 0.20 0.33 269
accuracy 0.80 1139
macro avg 0.83 0.60 0.61 1139
weighted avg 0.81 0.80 0.75 1139
accuracy of RandomForestClassifier-> 93.94205443371378
precision recall f1-score support
0 0.94 0.99 0.96 870
1 0.94 0.79 0.86 269
accuracy 0.94 1139
macro avg 0.94 0.89 0.91 1139
weighted avg 0.94 0.94 0.94 1139
accuracy of SVC-> 94.5566286215979
precision recall f1-score support
0 0.95 0.98 0.96 870
1 0.93 0.84 0.88 269
accuracy 0.95 1139
macro avg 0.94 0.91 0.92 1139
weighted avg 0.94 0.95 0.94 1139
word2vec
accuracy of MultinomialNB-> 76.82177348551362
precision recall f1-score support
0 0.77 1.00 0.87 870
1 1.00 0.02 0.04 269
accuracy 0.77 1139
macro avg 0.88 0.51 0.45 1139
weighted avg 0.82 0.77 0.67 1139
accuracy of RandomForestClassifier-> 96.57594381035996
precision recall f1-score support
0 0.97 0.99 0.98 870
1 0.97 0.88 0.92 269
accuracy 0.97 1139
macro avg 0.97 0.94 0.95 1139
weighted avg 0.97 0.97 0.97 1139
accuracy of SVC-> 96.7515364354697
precision recall f1-score support
0 0.98 0.97 0.98 870
1 0.92 0.94 0.93 269
accuracy 0.97 1139
macro avg 0.95 0.96 0.96 1139
weighted avg 0.97 0.97 0.97 1139
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