sklearn之样本生成
make_classification，
make_circles和make_moons
python np.logspace(1,10,5)
np.linspace() 创建等比数列，生成(start,stop)区间指定元素个数num的list，均匀分布
np.logspace() log分布间距生成list
np.arange() 生成(start,stop)区间指定步长step的list
numpy库：常用基本
https://www.cnblogs.com/smallpi/p/4550361.html
scikit-learn 中文文档
http://cwiki.apachecn.org/display/sklearn/Index
http://sklearn.apachecn.org/#/ （需要翻墙）
模型评估: 量化预测的质量
https://blog.csdn.net/marsjhao/article/details/78678276
30分钟学会用scikit-learn的基本回归方法（线性、决策树、SVM、KNN）和集成方法（随机森林，Adaboost和GBRT）
https://blog.csdn.net/u010900574/article/details/52666291
很值得看的特征选择 方法
https://www.cnblogs.com/stevenlk/p/6543628.html
XGboost数据比赛实战之调参篇
https://blog.csdn.net/sinat_35512245/article/details/79700029
https://blog.csdn.net/han_xiaoyang/article/details/52665396
Scikit中的特征选择，XGboost进行回归预测，模型优化的完整过程
https://blog.csdn.net/sinat_35512245/article/details/79668363
机器学习入门--协同过滤算法[推荐算法]
https://blog.csdn.net/u012995888/article/details/79077681
TFIDF介绍
https://www.cnblogs.com/cppb/p/5976266.html
pyspark
http://www.code123.cc/1499.html
http://blog.jobbole.com/86232
sklearn线性回归,支持向量机SVR回归,随机森林回归,神经网络回归参数解释及示例
https://blog.csdn.net/manjhOK/article/details/80367624
LR模型常见问题小议
https://blog.csdn.net/starzhou/article/details/52220070
基于Python的信用评分卡模型分析
https://www.jianshu.com/p/f931a4df202c
一文搞定BP神经网络——从原理到应用（原理篇）
https://blog.csdn.net/u014303046/article/details/78200010
分类中解决类别不平衡问题
https://blog.csdn.net/program_developer/article/details/80287033
类别不平衡问题之SMOTE算法（Python imblearn极简实现）
https://blog.csdn.net/nlpuser/article/details/81265614
https://imbalanced-learn.org/en/stable/generated/imblearn.over_sampling.SMOTE.html
Lightgbm基本原理介绍
https://blog.csdn.net/qq_24519677/article/details/82811215
https://www.jianshu.com/p/b4ac0596e5ef
异常检测算法--Isolation Forest
https://www.cnblogs.com/fengfenggirl/p/iForest.html
https://blog.csdn.net/ye1215172385/article/details/79762317
RF,GBDT,XGBoost,lightGBM对比分析
https://blogsklearncsdn.net/zhangbaoanhadoop/article/details/81948726
GridSearchCV 与 RandomizedSearchCV 调参
https://blog.csdn.net/juezhanangle/article/details/80051256
http://www.pianshen.com/article/7662198758/
Python超参数自动搜索模块GridSearchCV上手
https://www.cnblogs.com/nwpuxuezha/p/6618205.html
sklearn浅析（一）——sklearn的组织结构
https://blog.csdn.net/qsczse943062710/article/details/75642666
Hive 窗口函数、分析函数
https://www.cnblogs.com/skyEva/p/5730531.html
Hive常用函数大全（二）（窗口函数、分析函数、增强group）
https://blog.csdn.net/scgaliguodong123_/article/details/60135385
Hive窗口函数 （非常详细）
https://blog.csdn.net/qq_26937525/article/details/54925827
特征选择 (feature_selection)
https://www.cnblogs.com/stevenlk/p/6543628.html
from pyspark import SparkConf, SparkContext
conf = SparkConf().setMaster("local").setAppName("My App")
sc = SparkContext(conf = conf)
lines = sc.textFile("first.py")
pythonLines = lines.filter(lambda line: "Python" in line)
print "hello python"
print pythonLines.first()
print pythonLines.first()
print "hello spark!"
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