Ben Heubl BenHeubl
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| model_random_forrest_optimal <- randomForest(INCOME ~ ., | |
| data = TrainSet, | |
| ntree = 500, mtry = 3, | |
| importance = TRUE) | |
| model_decision_RF = predict(model_random_forrest_optimal, data = TrainSet) | |
| table(model_decision_RF, TrainSet$INCOME) | |
| mean(model_decision_RF == TrainSet$INCOME) | |
| #[1] 0.7757143 |
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| install.packages("rpart") | |
| install.packages("caret") | |
| install.packages("e1071") | |
| library(rpart) | |
| library(caret) | |
| library(e1071) | |
| model_decision_tree = train(INCOME ~ ., data = TrainSet, method = "rpart") | |
| model_decision_tree_prediction = predict(model_decision_tree, data = TrainSet) |
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| # Using For loop to identify the right mtry for model (this took around 4 minutes for me. Get yourself a drink :-) | |
| accuracy_list =c() | |
| for (i in 3:8) { | |
| print(i) | |
| model_optimal <- randomForest(INCOME ~ ., data = TrainSet, ntree = 500, mtry = i, importance = TRUE) | |
| predValid <- predict(model_optimal, ValidSet, type = "class") | |
| accuracy_list[i-2] = mean(predValid == ValidSet$INCOME) | |
| } |
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| income <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/income.csv") | |
| incomeR <- income %>% | |
| mutate(INCOME = if_else(INCOME == "-10.000)", "Under 30k", | |
| if_else(INCOME == "[10.000–15.000)", "Under 30k", | |
| if_else(INCOME == "[15.000–20.000)", "Under 30k", | |
| if_else(INCOME == "[20.000–25.000)", "Under 30k", | |
| if_else(INCOME == "[25.000–30.000)", "Under 30k", 'Over 30k')))))) %>% mutate_if(is.factor, fct_explicit_na, na_level = 'Unknown') %>% | |
| mutate(INCOME = as.factor(INCOME)) |
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| #mode function | |
| getmode <- function(v) { | |
| uniqv <- unique(v) | |
| uniqv[which.max(tabulate(match(v, uniqv)))] | |
| } | |
| incomeR_mode_income <- incomeR %>% | |
| group_by(INCOME) %>% | |
| summarise(mode = getmode(OCCUPATION)) | |
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| # ggplotting our featuer importance: | |
| Feature_importance <- importance(model_base) | |
| var_Importance <- data.frame(Variables = row.names(Feature_importance), | |
| Importance = round(importance[ ,'MeanDecreaseGini'],2)) | |
| #Create ranks for variable based on importance | |
| Rank_Importance <- var_Importance %>% | |
| mutate(Rank = paste0('#',dense_rank(desc(Importance)))) | |
| #Relative importance of our varaibles |
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| # read in the data from github repo: | |
| income <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/income.csv") | |
| set.seed(100) | |
| # We shuffle row-wise: | |
| incomeR <- income[sample(nrow(income)),] | |
| #check rownames (see above screenshot) | |
| colnames(incomeR) |
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| install.packages("relaimpo") | |
| library(relaimpo) | |
| #fit linear model: | |
| Ozone_model <- lm(ozone_reading ~ . , data = Ozone) | |
| #Get relative importance: | |
| Relative_importance <- calc.relimp(lmMod, type = "lmg", rela = TRUE) | |
| # Relative importance scaled to 100 and plot: |
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| install.packages('PerformanceAnalytics') | |
| library(PerformanceAnalytics) | |
| chart.Correlation(Ozone, histogram=TRUE, pch=19) |
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| #read in data | |
| Ozone <- read.csv("https://raw.githubusercontent.com/selva86/datasets/master/ozone.csv", stringsAsFactors=F) |
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