params.yaml

# Global parameters
random_state: 42
target: "loan_status"
train_file: "data/processed/train.csv"
test_file: "data/processed/test.csv"
train_metrics_file: "reports/train_metrics.json"
test_metrics_file: "reports/test_metrics.json"
train_calibration_curve_file: "reports/figures/train_calibration_curve.png"
test_calibration_curve_file: "reports/figures/test_calibration_curve.png"
model_file: "models/woelr.pkl"

# Parameters for data preprocessing
data_preprocessing:
  raw_data_file: "data/raw/credit_risk_dataset.csv"
  test_size: 0.3

# Parameters about model training
train:
  weight_of_evidence_transformer:
    numerical_columns: ["person_age", "person_income", "person_emp_length", "loan_amnt", "loan_int_rate", "loan_percent_income", "cb_person_cred_hist_length"]
    categorical_columns: ["person_home_ownership", "loan_intent", "loan_grade", "cb_person_default_on_file"]
    bins: 8
  logistic_regression:
    random_state: 42

# Parameters containing information for experiment tracking
mlflow:
  experiment_name: "weight-of-evidence-logistic-regression-exp"
  model_name: "woelr"
  remote_uri: "https://dagshub.com/marcellinus-witarsah/credit-score-mlops.mlflow"