GemStone-Price-Prediction/models.yaml

DecisionTree Regressor: 
 
  ccp_alpha: 0.0
 
  criterion: squared_error

  min_impurity_decrease: 0.0
  min_samples_leaf: 1
  min_samples_split: 2
  min_weight_fraction_leaf: 0.0

  splitter: best
ElasticNet Regression: 
 
  alpha: 1.0
  copy_X: true
  fit_intercept: true
  l1_ratio: 0.5
  max_iter: 1000
  positive: false
  precompute: false

  selection: cyclic
  tol: 0.0001
  warm_start: false
Lasso Regression: 
 
  alpha: 1.0
  copy_X: true
  fit_intercept: true
 
  max_iter: 1000
  positive: false
  precompute: false

  selection: cyclic
  tol: 0.0001
  warm_start: false
# Liner Regression: 
 
#   copy_X: true
#   fit_intercept: true
#   n_jobs: -1
#   positive: false
RandomForest Regressor:
 
  bootstrap: true
  ccp_alpha: 0.0
 
  criterion: squared_error
  # estimator: .DecisionTreeRegressor
  #   #
  #   # ccp_alpha: 0.0
  #   # class_weight: null
  #   # criterion: squared_error
  #   # max_depth: null
  #   # max_features: null
  #   # max_leaf_nodes: null
  #   # min_impurity_decrease: 0.0
  #   # min_samples_leaf: 1
  #   # min_samples_split: 2
  #   # min_weight_fraction_leaf: 0.0
  #   # monotonic_cst: null
  #   # random_state: null
  #   # splitter: best
  # estimator_params:
  # - criterion
  # - max_depth
  # - min_samples_split
  # - min_samples_leaf
  # - min_weight_fraction_leaf
  # - max_features
  # - max_leaf_nodes
  # - min_impurity_decrease
  # - random_state
  # - ccp_alpha
  # - monotonic_cst
  max_depth: null
  max_features: 1.0
 

  min_impurity_decrease: 0.0
  min_samples_leaf: 1
  min_samples_split: 2
  min_weight_fraction_leaf: 0.0
 
  n_estimators: 100

  oob_score: false

  verbose: 0
  warm_start: false
# Ridge Regeassor:
 
#   alpha: 1.0
#   copy_X: true
#   fit_intercept: true
#   max_iter: null
#   positive: false

#   solver: auto
  # tol: 0.0001