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jmetalpy icon jmetalpy

A framework for single/multi-objective optimization with metaheuristics

kaggle_house_prices icon kaggle_house_prices

问题定义，下载数据，观察结构，找最相关部分特征； 除去异常值，填充缺省值，部分特征正态化，特征编码向量化，增加新特征； 定义评估标准，选择表现好的几个模型，模型参数最优化，stacking模型融合。最终误差降低到0.101左右。详细介绍见https://zhuanlan.zhihu.com/p/89923216

kingcounty icon kingcounty

美国King County房价预测训练赛 神经网络

optimize_model icon optimize_model

本项目主要功能是自动完成特征选择与参数调试，用于优化随机森林模型，本项目的特征文件来自于文献“Two-layer detection framework with a high accuracy and efficiency for a malware family over the TLS protocol”中的部分研究。

qh_finsight icon qh_finsight

国内首个迁移学习赛题 **平安前海征信“好信杯”迁移学习大数据算法大赛 FInSight团队作品(算法方案排名第三)

qlm-tianchi icon qlm-tianchi

天池大数据竞赛 千里马大赛 风险识别与预测赛题 Top5

reinforcement-learning icon reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

student_and_teacher_classifier icon student_and_teacher_classifier

通过科研人员论文项目等数据，训练识别导师/学生的分类器。代码包括特征选择基础、网格搜索确定特征选择方法参数、不平衡数据的处理(oversampling、undersampling)和pu-learning方法在此问题上的应用

textclassification-meituan-featureextraction-classifier icon textclassification-meituan-featureextraction-classifier

该工程是利用python3.6进行“特征提取+分类器”来实现美团评论的文本二分类问题。在特征提取部分提取了6种特征，分类器选择了python里面的包xgboost和lightGBM分别实现提升树和GBDT（梯度提升决策树）。

text_classification_ai100 icon text_classification_ai100

AI100竞赛：http：//competition.ai100.com.cn/html/game_det.html？id = 24＆tab = 1 的代码，主要用于文本分类，其中涉及CHI选择特征词，TFIDF计算权重，朴素贝叶斯，决策树，SVM，XGBoost等算法

xgboost-featureselection icon xgboost-featureselection

此处利用 XGBoost 进行特征选择,选择对最终结果有较大影响的因子，运行代码可得出各个特征的影响排序、ROC图、AUC值、各个特征之间的相关系数热力图等

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