Baseline Model trained on pruned_datavq__ydnj to apply classification on is_phishing
Metrics of the best model:
accuracy 1.0
average_precision 1.0
roc_auc 1.0
recall_macro 1.0
f1_macro 1.0
Name: DecisionTreeClassifier(class_weight='balanced', max_depth=1), dtype: float64
See model plot below:
Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
id True False False ... False False False
bad_domain False False False ... False True False
safe_domain False False False ... False False False[3 rows x 7 columns])),('decisiontreeclassifier',DecisionTreeClassifier(class_weight='balanced', max_depth=1))])
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Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
id True False False ... False False False
bad_domain False False False ... False True False
safe_domain False False False ... False False False[3 rows x 7 columns])),('decisiontreeclassifier',DecisionTreeClassifier(class_weight='balanced', max_depth=1))])EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless id True False False ... False False False bad_domain False False False ... False True False safe_domain False False False ... False False False[3 rows x 7 columns])
DecisionTreeClassifier(class_weight='balanced', max_depth=1)
Disclaimer: This model is trained with dabl library as a baseline, for better results, use AutoTrain.
Logs of training including the models tried in the process can be found in logs.txt