xuman-Amy / preprocessingLinks
数据预处理之缺失值处理,特征选择
☆22Updated 6 years ago
Alternatives and similar repositories for preprocessing
Users that are interested in preprocessing are comparing it to the libraries listed below
Sorting:
- 常用的特征选择方法☆67Updated 3 years ago
- 基于遗传算法的特征选择☆128Updated 5 years ago
- 机器学习的特征工程,包括特 征抽取、特征预处理、特征选择、特征降维。☆25Updated 6 years ago
- Bayesian Optimization and Grid Search for xgboost/lightgbm☆78Updated 9 months ago
- Transfer Learning JDA and TrAdaboost☆65Updated 7 years ago
- Oversampling for imbalanced learning based on k-means and SMOTE☆128Updated 4 years ago
- Oversampling method based on relative density☆13Updated 5 years ago
- Time Series Prediction, Stateful LSTM; 时间序列预测,洗发水销量/股票走势预测,有状态循环神经网络☆57Updated 8 years ago
- TensorFlow Probability;Time series model☆127Updated 3 years ago
- CCF大数据与计算智能大赛-工件检测TOP1方案☆29Updated 5 years ago
- 机器学习集成模型之Stacking各类模型及工具源码☆118Updated 5 years ago
- 交易欺诈作为信用卡行业面临的主要贷后风险业务问题,每年都使信用卡行业遭受巨额损失。基于大数据机器学习开发出高效的交易欺诈识别模型一直是金融行业的主要挑战之一。本次大赛以此作为主题☆45Updated 6 years ago
- 国内首个迁移学习赛题 中国平安前海征信“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)☆87Updated 7 years ago
- 包括决策树和随机森林进行离职人员预测,Xgboost和lightGBM的应用☆287Updated 5 years ago
- Keras version of LSTNet☆96Updated 6 years ago
- 基于Keras的LSTM多变量时间序列预测☆184Updated 7 years ago
- Cost-Sensitive Learning / ReSampling / Weighting / Thresholding / BorderlineSMOTE / AdaCost / etc.☆109Updated 4 years ago
- 第三届“融360”天机智能算法挑战赛中“拒绝推断”赛题--复赛第四名的代码分享☆11Updated 5 years ago
- 使用sklearn做特征工程☆176Updated 7 years ago
- 利用时间序列预测汽车销量☆43Updated 7 years ago
- 在sklearn下,几种常用的特征选择方法☆41Updated 9 years ago
- 时间序列理论和案例实践☆72Updated 8 years ago
- feature selections and extractions☆89Updated last year
- Affinity Propagation Clustering with DTW distance on temporal sequence classification☆20Updated 6 years ago
- Stacking classification and regression☆25Updated 6 years ago
- Solutions of the forecast problem using Xgboost☆92Updated 6 years ago
- kaggle: IEEE-CIS Fraud Detection☆30Updated 6 years ago
- ☆13Updated 6 years ago
- There are some reproduced algorithms for learning from imbalanced data, including over-sampling,under-sampling and boosting☆13Updated 2 years ago
- Using K-NN, SVM, Bayes, LSTM, and multi-variable LSTM models on time series forecasting☆52Updated 6 years ago