randomized_search

https://catboost.ai/en/docs/concepts/python-reference_catboostregressor_randomized_search#example

  1. from catboost import CatBoostRegressor
  3. train_data = [[1, 4, 5, 6],
  4.               [4, 5, 6, 7],
  5.               [30, 40, 50, 60],
  6.               [20, 30, 70, 60],
  7.               [10, 80, 40, 30],
  8.               [10, 10, 20, 30]]
  9. train_labels = [10, 20, 30, 15, 10, 25]
  10. model = CatBoostRegressor()
  12. grid = {'learning_rate': [0.03, 0.1],
  13.         'depth': [4, 6, 10],
  14.         'l2_leaf_reg': [1, 3, 5, 7, 9]}
  16. randomized_search_result = model.randomized_search(grid,
  17.                                                    X=train_data,
  18.                                                    y=train_labels,
  19.                                                    plot=True)

grid_search

https://catboost.ai/en/docs/concepts/python-reference_catboostregressor_grid_search#example

  4. from catboost import CatBoostRegressor
  5. import numpy as np
  7. train_data = np.random.randint(1, 100, size=(100, 10))
  8. train_labels = np.random.randint(2, size=(100))
  10. model = CatBoostRegressor()
  12. grid = {'learning_rate': [0.03, 0.1],
  13.         'depth': [4, 6, 10],
  14.         'l2_leaf_reg': [1, 3, 5, 7, 9]}
  16. grid_search_result = model.grid_search(grid,
  17.                                        X=train_data,
  18.                                        y=train_labels,
  19.                                        plot=True)
  1. cb_model = CatBoostRegressor(iterations=700,
  2.                              learning_rate=0.02,
  3.                              depth=12,
  4.                              eval_metric='RMSE',
  5.                              random_seed = 23,
  6.                              bagging_temperature = 0.2,
  7.                              od_type='Iter',
  8.                              metric_period = 75,
  9.                              od_wait=100)