params.yaml

base:
  project: classification_mlops
  random_state: 42
  target_col: fruit_label
  features: ['mass', 'width', 'height', 'color_score']
  algorithms: ['logistic regression','Decision Tree', 'KNeighbors Classifier']

data_source:
  Git: data_given/fruit_data_with_colors.txt

load_data:
  raw_dataset_csv: data/raw/rawdata.csv


split_data:
  train_path: data/processed/train_data.csv
  test_path: data/processed/test_data.csv
  test_size: 0.25

estimators:
  ElasticNet:
    params:
      # alpha: 0.88
      # l1_ratio: 0.89
      # alpha: 0.9
      # l1_ratio: 0.4
      alpha: 0.5
      l1_ratio: 0.5
model_dir: saved_models

reports:
  params: report/params.json
  scores: report/scores.json

webapp_model_dir: prediction_service/model/model.joblib

mlflow_config:
  artifacts_dir: artifacts
  experiment_name: ElasticNet regression
  run_name: mlops
  registered_model_name: ElasticNetWineModel
  remote_server_uri: http://0.0.0.0:1234