Otto Product Classification Winner's Interview: 2nd place, Alexander Guschin ¯\_(ツ)_/¯ The Otto Group Product Classification Challenge made Kaggle history as our most popular competition ever. Alexander Guschin finished in 2nd place ahead of 3,845 ot…

Recruit Coupon Purchase Winner's Interview: 2nd place, Halla Yang Recruit Ponpare is Japan's leading joint coupon site, offering huge discounts on everything from hot yoga, to gourmet sushi, to a summer concert bonanza. The Recruit Coupon Purchase Pr…

How Much Did It Rain? Winner's Interview: 1st place, Devin Anzelmo An early insight into the importance of splitting the data on the number of radar scans in each row helped Devin Anzelmo take first place in the How Much Did It Rain? competition. In…

CrowdFlower Winner's Interview: 1st place, Chenglong Chen The Crowdflower Search Results Relevance competition asked Kagglers to evaluate the accuracy of e-commerce search engines on a scale of 1-4 using a dataset of queries & results. Chenglong Chen…

Liberty Mutual Property Inspection, Winner's Interview: Qingchen Wang The hugely popular Liberty Mutual Group: Property Inspection Prediction competition wrapped up on August 28, 2015 with Qingchen Wang at the top of a crowded leaderboard. A total of…

Facebook IV Winner's Interview: 1st place, Peter Best (aka fakeplastictrees) Peter Best (aka fakeplastictrees) took 1st place in Human or Robot?, our fourth Facebook recruiting competition. Finishing ahead of 984 other data scientists, Peter ignored…

ICDM Winner's Interview: 3rd place, Roberto Diaz This summer, the ICDM 2015 conference sponsored a competitionfocused on making individual user connections across multiple digital devices. Top teams were invited to submit a paper for presentation at…

Diabetic Retinopathy Winner's Interview: 1st place, Ben Graham Ben Graham finished at the top of the leaderboard in the high-profileDiabetic Retinopathy competition. In this blog, he shares his approach on a high-level with key takeaways. Ben finishe…

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目录 5.3 使用LogisticRegressionCV进行正则化的 Logistic Regression 参数调优 一.Scikit Learn中有关logistics回归函数的介绍 1. 交叉验证 交叉验证用于评估模型性能和进行参数调优(模型选择).分类任务中交叉验证缺省是采用StratifiedKFold. sklearn.cross_validation.cross_val_score(estimator, X, y=None, scoring=None, cv=None, n_jo…