ds: 2r, 4r, 5r, 8r

ds/25-1/r/8.R

@@ -0,0 +1,65 @@
+setwd('/home/sek1ro/git/public/lab/ds/25-1/r')
+df = read.csv("nbtrain.csv", stringsAsFactors = TRUE)
+trdf = df[1:9010,]
+tedf = df[9011:10010,]
+remove(df)
+library(e1071)
+
+nb = naiveBayes(income ~ age + sex + educ, data = trdf, laplace = 1)
+# p(A|B)*p(B) = p(AB) = p(B|A)*p(A)
+# p(A|B) = p(B|A) * p(A) / p(B)
+# апостер = услов * априор / маргин
+nb$apriori / sum (nb$apriori)
+nb$tables
+
+
+pd = predict(nb, tedf)
+(conf_mat = table(Actual = tedf$income, Predicted = pd))
+
+conf_tot = function(conf_mat) {
+  cat(1 - (sum(diag(conf_mat)) / sum(conf_mat)))
+}
+
+conf_class = function(conf_mat) {
+  for (income in rownames(conf_mat)) {
+    err = 1 - (conf_mat[income, income] / sum(conf_mat[income, ]))
+    cat(sprintf("%s error %.2f%%\n", income, err * 100))
+  }
+}
+
+conf_tot(conf_mat)
+conf_class(conf_mat)
+
+nb = naiveBayes(sex ~ age + educ + income, data = trdf, laplace = 1)
+nb$apriori / sum (nb$apriori)
+nb$tables
+
+pd = predict(nb, tedf)
+(conf_mat = table(Actual = tedf$sex, Predicted = pd))
+conf_tot(conf_mat)
+conf_class(conf_mat)
+
+
+
+male = trdf[trdf$sex == "M", ]
+female = trdf[trdf$sex == "F", ]
+
+nbrandom = function() {
+  mdf = male[sample(1:nrow(male), 3500),]
+  fdf = female[sample(1:nrow(female), 3500), ]
+  
+  mfdf = rbind(mdf, fdf)
+  
+  mfnb = naiveBayes(sex ~ age + educ + income, data = mfdf, laplace = 1)
+  mfnb$apriori / sum (mfnb$apriori)
+  mfnb$tables
+  
+  mfpd = predict(mfnb, tedf)
+  (mfconf_mat = table(Actual = tedf$sex, Predicted = mfpd))
+  conf_tot(mfconf_mat)
+  conf_class(mfconf_mat)
+}
+
+set.seed(Sys.time())
+nbrandom()
+