{
  "_comment": "Model configuration template for the classification model builder plugin.",
  "model_name": "ExampleClassifier",
  "_comment": "A descriptive name for your model.",
  "model_type": "RandomForestClassifier",
  "_comment": "The type of classification model to use (e.g., RandomForestClassifier, LogisticRegression, SVM).",
  "data_path": "data/training_data.csv",
  "_comment": "Path to the CSV file containing the training data.",
  "target_column": "target",
  "_comment": "The name of the column containing the target variable.",
  "features": [
    "feature1",
    "feature2",
    "feature3",
    "feature4"
  ],
  "_comment": "List of column names to use as features. If empty, all columns except the target_column will be used.",
  "hyperparameters": {
    "_comment": "Hyperparameters specific to the chosen model type.",
    "n_estimators": 100,
    "_comment": "Number of trees in the random forest (example for RandomForestClassifier).",
    "max_depth": 10,
    "_comment": "Maximum depth of the trees (example for RandomForestClassifier).",
    "learning_rate": 0.1
    "_comment": "Learning rate for gradient boosting models (example for GradientBoostingClassifier)."
  },
  "training_parameters": {
    "_comment": "Parameters related to the training process.",
    "test_size": 0.2,
    "_comment": "The proportion of the data to use for testing.",
    "random_state": 42,
    "_comment": "A random seed for reproducibility.",
    "stratify": true
    "_comment": "Whether to stratify the target variable during train/test split."
  },
  "evaluation_metrics": [
    "accuracy",
    "precision",
    "recall",
    "f1-score",
    "roc_auc"
  ],
  "_comment": "List of evaluation metrics to compute on the test set.",
  "model_save_path": "models/example_classifier.pkl",
  "_comment": "Path to save the trained model.",
  "feature_importance": true,
  "_comment": "Boolean value to toggle feature importance calculation.",
  "preprocessing": {
    "_comment": "Configuration for data preprocessing steps.",
    "handle_missing_values": "impute",
    "_comment": "How to handle missing values (e.g., 'impute', 'remove', 'none').",
    "missing_value_strategy": "mean",
    "_comment": "Strategy for imputation (e.g., 'mean', 'median', 'most_frequent').",
    "scale_features": true,
    "_comment": "Whether to scale numerical features using StandardScaler or similar.",
    "feature_scaling_method": "standard"
    "_comment": "Method to use for feature scaling ('standard' or 'minmax')."
  }
}