[{"title":"( 22 个子文件 183KB ) 专门处理不平衡数据集的算法,使用21种采样的算法,包括SMOTE,集成算法+采样算法,基于聚类的过采样算法。对每一个算法原理,实验结果,评价标准都给了注释。","children":[{"title":"不平衡数据集","children":[{"title":"Under-sampling","children":[{"title":"ENN &RENN.ipynb <span style='color:#111;'> 9.95KB </span>","children":null,"spread":false},{"title":"下采样.ipynb <span style='color:#111;'> 17.69KB </span>","children":null,"spread":false},{"title":"NearMiss.ipynb <span style='color:#111;'> 13.09KB </span>","children":null,"spread":false},{"title":"基于聚类的抽样.ipynb <span style='color:#111;'> 6.50KB </span>","children":null,"spread":false},{"title":"随机欠采样.ipynb <span style='color:#111;'> 9.74KB </span>","children":null,"spread":false},{"title":".ipynb_checkpoints","children":[{"title":"ENN &RENN-checkpoint.ipynb <span style='color:#111;'> 72B </span>","children":null,"spread":false},{"title":"随机欠采样-checkpoint.ipynb <span style='color:#111;'> 6.16KB </span>","children":null,"spread":false},{"title":"ALLKNN-checkpoint.ipynb <span style='color:#111;'> 72B </span>","children":null,"spread":false},{"title":"NearMiss-checkpoint.ipynb <span style='color:#111;'> 13.09KB </span>","children":null,"spread":false},{"title":"下采样-checkpoint.ipynb <span style='color:#111;'> 17.69KB </span>","children":null,"spread":false}],"spread":true},{"title":"ALLKNN.ipynb <span style='color:#111;'> 6.14KB </span>","children":null,"spread":false}],"spread":true},{"title":"data","children":[{"title":"target.csv <span style='color:#111;'> 11.72KB </span>","children":null,"spread":false},{"title":"train.csv <span style='color:#111;'> 989.16KB </span>","children":null,"spread":false},{"title":"test.csv <span style='color:#111;'> 672.08KB </span>","children":null,"spread":false}],"spread":true},{"title":"oversample+undersample","children":[{"title":"过采样+欠采样.ipynb <span style='color:#111;'> 10.13KB </span>","children":null,"spread":false},{"title":".ipynb_checkpoints","children":[{"title":"过采样+欠采样-checkpoint.ipynb <span style='color:#111;'> 10.13KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"Ensemble","children":[{"title":"Ensemble.ipynb <span style='color:#111;'> 14.55KB </span>","children":null,"spread":false},{"title":".ipynb_checkpoints","children":[{"title":"Ensemble-checkpoint.ipynb <span style='color:#111;'> 14.55KB </span>","children":null,"spread":false}],"spread":true}],"spread":true},{"title":"Over-sampling","children":[{"title":"SMOTE.ipynb <span style='color:#111;'> 16.96KB </span>","children":null,"spread":false},{"title":"ADASYN.ipynb <span style='color:#111;'> 6.41KB </span>","children":null,"spread":false},{"title":"随机过采样.ipynb <span style='color:#111;'> 14.92KB </span>","children":null,"spread":false},{"title":".ipynb_checkpoints","children":[{"title":"随机过采样-checkpoint.ipynb <span style='color:#111;'> 14.92KB </span>","children":null,"spread":false}],"spread":true}],"spread":true}],"spread":true}],"spread":true}]