78 lines
1.8 KiB
Plaintext
78 lines
1.8 KiB
Plaintext
---
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title: "Lab8: Naive bayes classifier"
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author: "Vladislav Litvinov <vlad@sek1ro>"
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output:
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pdf_document:
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toc_float: TRUE
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---
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# Data splitting (test, train datasets)
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```{r}
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setwd('/home/sek1ro/git/public/lab/ds/25-1/r')
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df = read.csv("nbtrain.csv", stringsAsFactors = TRUE)
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trdf = df[1:9010,]
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tedf = df[9011:10010,]
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remove(df)
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library(e1071)
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```
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# Model training
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```{r}
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nb = naiveBayes(income ~ age + sex + educ, data = trdf, laplace = 1)
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# p(A|B)*p(B) = p(AB) = p(B|A)*p(A)
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# p(A|B) = p(B|A) * p(A) / p(B)
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# апостер = услов * априор / маргин
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nb$apriori / sum (nb$apriori)
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nb$tables
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```
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# Model testing. Confidence total and confidence by class
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```{r}
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pd = predict(nb, tedf)
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(conf_mat = table(Actual = tedf$income, Predicted = pd))
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conf_tot = function(conf_mat) {
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cat(1 - (sum(diag(conf_mat)) / sum(conf_mat)))
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}
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conf_class = function(conf_mat) {
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for (income in rownames(conf_mat)) {
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err = 1 - (conf_mat[income, income] / sum(conf_mat[income, ]))
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cat(sprintf("%s error %.2f%%\n", income, err * 100))
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}
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}
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conf_tot(conf_mat)
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conf_class(conf_mat)
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nb = naiveBayes(sex ~ age + educ + income, data = trdf, laplace = 1)
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nb$apriori / sum (nb$apriori)
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nb$tables
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pd = predict(nb, tedf)
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(conf_mat = table(Actual = tedf$sex, Predicted = pd))
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conf_tot(conf_mat)
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conf_class(conf_mat)
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```
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# Separated male and female
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```{r}
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male = trdf[trdf$sex == "M", ]
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female = trdf[trdf$sex == "F", ]
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nbrandom = function() {
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mdf = male[sample(1:nrow(male), 3500),]
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fdf = female[sample(1:nrow(female), 3500), ]
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mfdf = rbind(mdf, fdf)
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mfnb = naiveBayes(sex ~ age + educ + income, data = mfdf, laplace = 1)
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mfnb$apriori / sum (mfnb$apriori)
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mfnb$tables
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mfpd = predict(mfnb, tedf)
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(mfconf_mat = table(Actual = tedf$sex, Predicted = mfpd))
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conf_tot(mfconf_mat)
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conf_class(mfconf_mat)
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}
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set.seed(Sys.time())
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nbrandom()
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``` |