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Update evaluate.py
bb8ac88 verified - 1.52 kB initial commit
- 5.4 kB Update README.md
- 15 kB Fixing Feedback Loop
- 393 Bytes Upload 12 files
- 2.19 kB Update evaluate.py
- 10.2 kB Upload 12 files
- 31.5 kB Update ml_utils.py
- 1.66 kB Upload 12 files
- 57 Bytes added xgboost for ensemble models
- 117 kB dataset for training
- 504 kB Data training files
- 1.81 kB Upload 12 files
url_ensemble.pkl Detected Pickle imports (15)
- "sklearn.linear_model._logistic.LogisticRegression",
- "sklearn.feature_extraction.text.TfidfTransformer",
- "numpy._core.multiarray.scalar",
- "numpy._core.multiarray._reconstruct",
- "sklearn.tree._classes.DecisionTreeClassifier",
- "numpy.dtype",
- "sklearn.feature_extraction.text.TfidfVectorizer",
- "sklearn.pipeline.Pipeline",
- "builtins.bytearray",
- "sklearn.ensemble._forest.RandomForestClassifier",
- "numpy.ndarray",
- "sklearn.tree._tree.Tree",
- "xgboost.sklearn.XGBClassifier",
- "numpy.float64",
- "xgboost.core.Booster"
How to fix it?
2.06 MB Improved ML logic using Ensemble models & training on new dataset to include recent scam patterns like "Digital Arrests", "QR code scans"
