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metadata
library_name: sklearn
tags:
  - sklearn
  - skops
  - tabular-classification
  - finance
model_format: pickle
model_file: skops-ise057qg.pkl
widget:
  - structuredData:
      Bene_Country:
        - COMOROS
        - CANADA
        - MOROCCO
      Sender_Country:
        - SRI-LANKA
        - USA
        - USA
      Transaction_Type:
        - MOVE-FUNDS
        - PAY-CHECK
        - MAKE-PAYMENT
      USD_amount:
        - 598.31
        - 398.72
        - 87.03

Model description

This is a Gaussian Naive Bayes model trained on a synthetic dataset, containining a large variety of transaction types representing normal activities as well as abnormal/fraudulent activities generated by J.P. Morgan AI Research. The model predicts whether a transaction is normal or fraudulent.

Intended uses & limitations

For educational purposes

Training Procedure

The data preprocessing steps applied include the following:

  • Dropping high cardinality features. This includes Transaction ID, Sender ID, Sender Account, Beneficiary ID, Beneficiary Account, Sender Sector
  • Dropping no variance features. This includes Sender LOB
  • Dropping Time and date feature since the model is not time-series based
  • Transforming and Encoding categorical features namely: Sender Country, Beneficiary Country, Transaction Type, and the target variable, Label
  • Applying feature scaling on all features
  • Splitting the dataset into training/test set using 85/15 split ratio
  • Handling imbalanced dataset using imblearn framework and applying RandomUnderSampler method to eliminate noise which led to a 2.5% improvement in accuracy

image/png

Hyperparameters

Click to expand
Hyperparameter Value
memory
steps [('preprocessorAll', ColumnTransformer(remainder='passthrough',
transformers=[('cat',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore',
sparse_output=False))]),
['Sender_Country', 'Bene_Country',
'Transaction_Type']),
('num',
Pipeline(steps=[('scale', StandardScaler())]),
Index(['USD_amount'], dtype='object'))])), ('classifier', GaussianNB())]
verbose False
preprocessorAll ColumnTransformer(remainder='passthrough',
transformers=[('cat',
Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore',
sparse_output=False))]),
['Sender_Country', 'Bene_Country',
'Transaction_Type']),
('num',
Pipeline(steps=[('scale', StandardScaler())]),
Index(['USD_amount'], dtype='object'))])
classifier GaussianNB()
preprocessorAll__n_jobs
preprocessorAll__remainder passthrough
preprocessorAll__sparse_threshold 0.3
preprocessorAll__transformer_weights
preprocessorAll__transformers [('cat', Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore', sparse_output=False))]), ['Sender_Country', 'Bene_Country', 'Transaction_Type']), ('num', Pipeline(steps=[('scale', StandardScaler())]), Index(['USD_amount'], dtype='object'))]
preprocessorAll__verbose False
preprocessorAll__verbose_feature_names_out True
preprocessorAll__cat Pipeline(steps=[('onehot',
OneHotEncoder(handle_unknown='ignore', sparse_output=False))])
preprocessorAll__num Pipeline(steps=[('scale', StandardScaler())])
preprocessorAll__cat__memory
preprocessorAll__cat__steps [('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))]
preprocessorAll__cat__verbose False
preprocessorAll__cat__onehot OneHotEncoder(handle_unknown='ignore', sparse_output=False)
preprocessorAll__cat__onehot__categories auto
preprocessorAll__cat__onehot__drop
preprocessorAll__cat__onehot__dtype <class 'numpy.float64'>
preprocessorAll__cat__onehot__handle_unknown ignore
preprocessorAll__cat__onehot__max_categories
preprocessorAll__cat__onehot__min_frequency
preprocessorAll__cat__onehot__sparse deprecated
preprocessorAll__cat__onehot__sparse_output False
preprocessorAll__num__memory
preprocessorAll__num__steps [('scale', StandardScaler())]
preprocessorAll__num__verbose False
preprocessorAll__num__scale StandardScaler()
preprocessorAll__num__scale__copy True
preprocessorAll__num__scale__with_mean True
preprocessorAll__num__scale__with_std True
classifier__priors
classifier__var_smoothing 1e-09

Model Plot

Pipeline(steps=[('preprocessorAll',ColumnTransformer(remainder='passthrough',transformers=[('cat',Pipeline(steps=[('onehot',OneHotEncoder(handle_unknown='ignore',sparse_output=False))]),['Sender_Country','Bene_Country','Transaction_Type']),('num',Pipeline(steps=[('scale',StandardScaler())]),Index(['USD_amount'], dtype='object'))])),('classifier', GaussianNB())])
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Evaluation Results

Metric Value
accuracy 0.794582

Model Explainability

SHAP was used to determine the important features that helps the model make decisions image/png

Confusion Matrix

Confusion Matrix

Model Card Authors

This model card is written by following authors: Seifullah Bello