====== 回归及其他方法 ====== ===书籍=== 《机器学习那些事》 A Few Useful Things to Know about Machine Learning [[https://homes.cs.washington.edu/~pedrod/papers/cacm12.pdf]]\\ 《统计学习方法》李航 Python实现代码[[https://github.com/fengdu78/lihang-code]]\\ 《迁移学习简明手册》：[[https://github.com/jindongwang/transferlearning-tutorial]]\\ 《神经网络与深度学习》[[https://nndl.github.io/]]\\ 《面向机器学习的特征工程》[[https://github.com/apachecn/fe4ml-zh]]\\ Explanatory Model Analysis[[https://pbiecek.github.io/ema/]]\\ 《人工智能知识树》[[https://github.com/apachecn/ai-roadmap]]\\ ds-cheatsheets 速查手册[[https://github.com/FavioVazquez/ds-cheatsheets]]\\ ===短文=== 15 TYPES OF REGRESSION IN DATA SCIENCE[[https://www.listendata.com/2018/03/regression-analysis.html]]\\ Are categorical variables getting lost in your random forests?[[https://roamanalytics.com/2016/10/28/are-categorical-variables-getting-lost-in-your-random-forests/]]\\ Selecting good features[[http://blog.datadive.net/selecting-good-features-part-iii-random-forests/]]\\ Github半监督学习[[https://github.com/topics/semi-supervised-learning]]\\ Github特征工程[[https://github.com/topics/feature-engineering]]\\ A Data Science Framework: To Achieve 99% Accuracy[[https://www.kaggle.com/ldfreeman3/a-data-science-framework-to-achieve-99-accuracy#How-a-Data-Scientist-Beat-the-Odds]]\\ Machine Learning Mastery 博客文章翻译[[https://github.com/apachecn/ml-mastery-zh]]\\ ===实例=== 实例The Super Duper NLP Repo[[https://notebooks.quantumstat.com/]]\\ 实例Deep Learning Models[[https://github.com/rasbt/deeplearning-models]]\\ 我们为你精选了一份Jupyter/IPython笔记本集合[[https://www.jiqizhixin.com/articles/2019-04-23-3]]\\ Learn ML with clean code[[https://github.com/madewithml/lessons]]\\ Progressive Growing of GANs[[https://github.com/tkarras/progressive_growing_of_gans]]\\ Papers with code[[https://github.com/zziz/pwc]]\\ Good-Papers[[https://github.com/hoangcuong2011/Good-Papers]]\\ XGBoost with Label[[https://medium.com/@songxia.sophia/two-machine-learning-algorithms-to-predict-xgboost-neural-network-with-entity-embedding-caac68717dea]]\\ python-is-cool[[https://github.com/chiphuyen/python-is-cool]]\\ ====回归问题注意点==== ^ [[https://www.baidu.com/s?wd=多重共线性|多重共线性]] | Colinearity diagnostics(多重共线性诊断) | 差分法、岭回归、lasso、Elastic-Net等 | ^ [[https://www.baidu.com/s?wd=自相关性|自相关性]] | 残差图、Durbin-Watson Statistics(德宾—瓦特逊检验)、Q-Statistics | | ^ [[https://www.baidu.com/s?wd=异方差性|异方差性]] | 图示检验法、Goldfeld - Quandt 检验法、White检验法、Park检验法和Gleiser检验法 | 模型变换、加权最小二乘法 | ^ [[https://www.baidu.com/s?wd=交叉验证|交叉验证]]| | ====基础理论==== 最小二乘法、最大似然法 ====包说明==== >sklearn:[[http://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model]] ==== 分类回归 ==== ^ 方法 ^ 中文 ^ Python ^ R ^ ^ Linear Models | | [[http://scikit-learn.org/stable/modules/linear_model.html|Generalized Linear Models]] | | ^ logistic | | | | ^ Ordinary Least Squares | | [[http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LinearRegression.html#sklearn.linear_model.LinearRegression|sklearn.linear_model.LinearRegression]] | | ^ Stochastic Gradient Descent | 随机梯度下降 | [[http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier|sklearn.linear_model.SGDClassifier]] [[http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDRegressor.html#sklearn.linear_model.SGDRegressor|sklearn.linear_model.SGDRegressor]] | | ^ Cox | | | | ^ Ridge Regression | 岭回归 | [[http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Ridge.html#sklearn.linear_model.Ridge|sklearn.linear_model.Ridge]] | | ^ lasso | 套索回归 | [[http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lasso.html|sklearn.linear_model.Lasso]] | | ^ Elastic-Net | 弹性网回归 | [[http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.ElasticNet.html#sklearn.linear_model.ElasticNet|sklearn.linear_model.ElasticNet]] | | ^ Least-angle regression | | [[http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.Lars.html#sklearn.linear_model.Lars|sklearn.linear_model.Lars]] | | | | | | | ^ Ensemble methods | | [[http://scikit-learn.org/stable/modules/ensemble.html#ensemble|Ensemble methods]] | | ^ Decision Tree | 决策树 | | | ^ Boosted Decision Tree (GDBT) | | | | ^ Gradient Boosted Regression Trees (GBRT) | | [[http://scikit-learn.org/stable/modules/ensemble.html#gradient-tree-boosting|gradient-tree-boosting]] | | ^ AdaBoost | | [[http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostClassifier.html#sklearn.ensemble.AdaBoostClassifier|sklearn.ensemble.AdaBoostClassifier]] [[http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.AdaBoostRegressor.html#sklearn.ensemble.AdaBoostRegressor|sklearn.ensemble.AdaBoostRegressor]] | | ^ XGBoost | | [[https://xgboost.readthedocs.io/en/latest/|XGBoost]] [[https://github.com/dmlc/xgboost|GitHub]] | | ^ Random Forest | 随机森林 | [[http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier|sklearn.ensemble.RandomForestClassifier]] [[http://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor|sklearn.ensemble.RandomForestRegressor]] | | ^ SVM | | | | ===备注=== Lasso （Least Absolute Shrinkage and Selection Operator） Stepwise Regression 逐步回归 Bayesian、Ecological和Robust回归 ====Trees==== [[https://github.com/IBM/xgboost-smote-detect-fraud/blob/master/notebook/Fraud_Detection.ipynb]]XGB+SMOTE\\ [[https://github.com/andosa/treeinterpreter]] [[http://engineering.pivotal.io/post/interpreting-decision-trees-and-random-forests/|Article]] Tree Interpreter\\ ====AI==== [[https://github.com/ajbrock/BigGAN-PyTorch]] BigGAN Pytorch\\ ====线性回归==== |直线回归| |多重线性回归| |曲线回归| ===== 其他 ===== |时间序列分析| |meta分析| | 主成分分析 | |因子分析| | 聚类分析 | |判别分析|