统计学习方法PDF.doc

cart, bagging, random forest, boosting? cartclassification and regression treebreiman, friedman, olshen & stone (1984), quinlan (1993)missing data example: red class: x12+x22>=4.6green class: otherwise0111.1 1.1.1 ,t1.1.3 1satterthwate t2,wilcoxon1.2 1.2.1 t1.2.2 wilcoxon1.3 1.3.1lsdbonferronitukeyscheffesnk1.3.2kruscalwallisbonferronipwilcoxon1.4 1.4.1lsdbonferronitukeyscheffesnk1.4.2fridmanbonferronipwilcoxon*1 50t2 lsdbonferronitukeyscheffesnk*t*3 22.1 2.1.1 405pearson 2.1.2 4015 fishers2.1.3 402fishers2.2 2cr22.2.1 1405<25pearson 2405>25fishers2.2.2pearson wilcoxon2.2.3 pearson 2.3 rc2.2.1 1405<25pearson 2405>25fishers3pearson2.2.2pearson wilcoxonridit2.2.3 pearson 2.2.4 1wilcoxonridit2spearson2.4 2.4.1 1bc>40mcnemar 2bc<40 2.4.1 cc1mcnemar 2kappaspss,pearson(), kendall spearman/pearsonspearman.spearmanpearsonspearmanpearsonspearmanxykendalls tau-b -1-1pearson;spearman; kendall spearmankendallpearson kendall spearman 1 -/pearson 2, spearman kendall3 kendall pearsonspsscorrelatebivariatecorrelation coefficients3pearsonkendalls tau-bspearmanspearmanspearman/kendalls(kendall)wkn1knn1 n121.5,1,2.5,2.5,5,5,5,2.52,354,5,6(kendall)ukn1knnij1ij00.5kkij tttfp0.05utun=30umeansdescriptivesone-sample t test tindependent-samples t test tpaired sample t test tone-way anovononparametric test.crosstabs stroke_clean.savspss p6spss11. scaleage2. ordinalresult3. nominalclotsolv4. booleangender11 central tendencyvariability 2frequency distribution2age502 meanarithmetic mean3? 0? ? ? 3 mode4mode,44 geometric mean>33.1? ? ? how to analyze data using the average,5. (the elements of statistical learning)the elements of statistical ?(the elements of statistical learning)?data miningcourseramachine learningcoursera model thinking.-6. yxysupervised learningyxunsupervised learning7. yyy8. learning modelsmse0-19. (psa)dna10. glmkernelbootstrap.lasso-10.15- f(x1,.,xp)=f(x1)+.+f(xp)boosting (support vector machine)(prototype)xy->xyrandom forest?(the elements of statisticallearning)? :d .-ols olsols n(y?y)2 y y olsxols y=x rss rss=(xx)?1xyx kk nearest neighborkxy y=mean(yi|xink(x) nk(x) xkolsxyknnkolsknn (statistical decision theory)x,y p(x,y) (x,y) p(x,y)y f(x) f(x) f l(y,f(x)=(y?f(x)2 epe(f)=e(y?f(x)2=y?f(x)2p(dx,dy) f f ideax _con