def _check_is_regressor_loss(self, loss_function): is_regression = self._is_regression_objective(loss_function) or self. _is_multiregression_objective(loss_function) if isinstance(loss_function, str) and not is_regression: raise CatBoostError("Invalid loss_function='{}': for regressor use "...
def _check_is_regressor_loss(self, loss_function): is_regression = self._is_regression_objective(loss_function) or self. _is_multiregression_objective(loss_function) if isinstance(loss_function, str) and not is_regression: raise CatBoostError("Invalid loss_function='{}': for regressor use "...
def _check_is_regressor_loss(self, loss_function): is_regression = self._is_regression_objective(loss_function) or self. _is_multiregression_objective(loss_function) if isinstance(loss_function, str) and not is_regression: raise CatBoostError("Invalid loss_function='{}': for regressor use "...
loss_function : string, [default='RMSE'] 'RMSE' 'MAE' 'Quantile:alpha=value' 'LogLinQuantile:alpha=value' 'Poisson' 'MAPE' 'Lq:q=value' """ 实现scikit-learn API的CatBoost回归。 参数 --- 像CatBoostClassifier,除了loss_function, classes_count, class_names和class_weights def __init__( s...
loss_function='RMSE' 5 eval_metric='RMSE' 6 random_seed=99 7 od_type='Iter' 8 od_wait=50 关键代码如下: 7.模型评估 7.1评估指标及结果 评估指标主要包括可解释方差值、平均绝对误差、均方误差、R方值等等。 模型名称 指标名称 指标值 测试集 CatBoost回归模型 可解释方差值 0.93 平均绝对误差 0.18...
# 需要導入模塊: import catboost [as 別名]# 或者: from catboost importCatBoostRegressor[as 別名]defCatBoost_First(self, data, catsign, depth=8, iterations=80000):model = cb.CatBoostRegressor(iterations=iterations, depth=depth, learning_rate=0.8, loss_function='RMSE') ...
model = catboost.CatBoostRegressor(loss_function='Quantile:alpha=0.95', ...) *I can maybe take a look at it and work on it, but will take time. It would be a good first contribution to do for me :) Thanks in advance, 👍 1 fjpa121197 added the Enhancement label Aug 19, 2022 ...
通常的做法是在训练数据再中分出一部分做为验证(Validation)数据，用来评估模型的训练效果。
loss_function : string, [default='RMSE'] 'RMSE' 'MAE' 'Quantile:alpha=value' 'LogLinQuantile:alpha=value' 'Poisson' 'MAPE' 'Lq:q=value' """ 实现scikit-learn API的CatBoost回归。 参数 --- 像CatBoostClassifier,除了loss_function, classes_count, class_names和class_weights def __init...
loss_function : string, [default='RMSE'] 'RMSE' 'MAE' 'Quantile:alpha=value' 'LogLinQuantile:alpha=value' 'Poisson' 'MAPE' 'Lq:q=value' """ 实现scikit-learn API的CatBoost回归。 参数 --- 像CatBoostClassifier,除了loss_function, classes_count, class_names和class_weights def __init...