params.yaml

# XGBoost model parameters
xgboost:
  learning_rate: 0.1
  max_depth: 5
  n_estimators: 100
  subsample: 0.8
  colsample_bytree: 0.8
  objective: 'reg:squarederror'
  random_state: 42
  # Add tree_method parameter to avoid GPU-related errors
  tree_method: 'hist'
  # Disable GPU acceleration if causing issues
  gpu_id: -1

# Random Forest model parameters
random_forest:
  n_estimators: 100
  max_depth: 10
  min_samples_split: 2
  min_samples_leaf: 1
  random_state: 42

# Linear Regression model parameters
linear_regression:
  fit_intercept: true
  # normalize parameter is deprecated in newer scikit-learn versions
  # normalize: false

# SVR model parameters
svr:
  kernel: 'rbf'
  C: 1.0
  epsilon: 0.1
  gamma: 'scale'

# KNN model parameters
knn:
  n_neighbors: 5
  weights: 'uniform'
  algorithm: 'auto'
  p: 2