This project forked from eryueniaobp/contest
License: Apache License 2.0
特征工程： 交叉特征 + 统计比率
baseline: 交叉特征 + 统计比率 + gbdt
单模型调优:
模型融合: stacking 4-folds .
gbdt 有效
模型融合有效
特征工程决定最终效果上限 ；过多特征造成 模型调优困难
NN 耗费资源，单NN效果可能略好于调好的 gbdt； relu造成节点萎缩，慎用 ； feature_size * nn_size 容易OOM ， nn_size 第一层一般为 512 256
输入： 原始数据
输出： libsvm样本
一般步骤:
上述样本构造出来以后，可以作为baseline 训练，查验基本的auc，logloss等指标
特征工程做不扎实，直接影响后续的 模型效果
各项维度的 统计均值 是工程难点
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