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))])
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
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
- Downloads last month
- -