第二个用例是使用make_scorer从简单的 python 函数构建一个完全 custom scorer object (自定义的记分对象),可以使用几个参数 : 你要使用的 python 函数(在下面的示例中是my_custom_loss_func) python 函数是否返回一个分数 (greater_is_better=True, 默认值) 或者一个 loss (损失) (greater_is_better=False)...
0.693, given the values for X>>>#and y defined below.>>> score = make_scorer(my_custom_loss_func, greater_is_better=False)>>> X = [[1], [1]]>>> y = [0, 1]>>>fromsklearn.dummyimportDummyClassifier>>> clf = DummyClassifier(strategy='most_frequent', random...
greater_is_better=False)>>> score = make_scorer(my_custom_loss_func, greater_is_better=True)>>> ground_truth = [[1, 1]]>>> predictions = [0, 1]
你可以使用python函数:下例中的my_custom_loss_func python函数是否返回一个score(greater_is_better=True),还是返回一个loss(greater_is_better=False)。如果为loss,python函数的输出将被scorer对象忽略,根据交叉验证的原则,得分越高模型越好。 对于分类问题的metrics:如果你提供的python函数是否需要对连续值进行决策判...
importnumpy as npdefmy_custom_loss_func(ground_truth, predictions): diff= np.abs(ground_truth -predictions).max()returnnp.log(1 +diff)#loss_func will negate the return value of my_custom_loss_func,#which will be np.log(2), 0.693, given the values for ground_truth#and predictions defi...
loss = make_scorer(my_custom_loss_func, greater_is_better=False) # 自定义度量对象。结果越小越好。greater_is_better设置为false,系统认为是损失函数,则会将计分函数取反 score = make_scorer(my_custom_loss_func, greater_is_better=True) # 自定义度量对象。结果越大越好 ...
def loss_function(y_true, y_pred):***some calculation*** return loss 创建均方误差损失函数(RMSE...
我使用了ligth GBM算法,并创建了一个类似于以下内容的管道: #model definition model_lgbm = LGBMClassifier( #training loss objective='binary', # write a custom objective function that is cost sensitive n_estimators = params['n_estimators'], max_ 浏览128提问于2021-08-16得票数 0 回答已采纳 ...
NOTE that when using custom scorers, each scorer should return a single value. Metric functions returning a list/array of values can be wrapped into multiple scorers that return one value each. See :ref:`multimetric_grid_search` for an example. If None, the estimator's default scorer (if ...
a custom objective function to be used (see note below). booster: string Specify which booster to use: gbtree, gblinear or dart. nthread : int Number of parallel threads used to run xgboost. (Deprecated, please use ``n_jobs``)