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View Code? Open in Web Editor NEWBasic data mining model, including feature importance display
Basic data mining model, including feature importance display
您好,大佬,lgb二分类中预测的都是0类别,感觉有问题啊
multi_class_custom_loss.py 中其实是自定义了评价指标,并不是loss哦。下面是官方的例子
# self-defined objective function
# f(preds: array, train_data: Dataset) -> grad: array, hess: array
# log likelihood loss
def loglikelihood(preds, train_data):
labels = train_data.get_label()
preds = 1. / (1. + np.exp(-preds))
grad = preds - labels
hess = preds * (1. - preds)
return grad, hess
# self-defined eval metric
# f(preds: array, train_data: Dataset) -> name: string, eval_result: float, is_higher_better: bool
# binary error
def binary_error(preds, train_data):
labels = train_data.get_label()
return 'error', np.mean(labels != (preds > 0.5)), False
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