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18a911f9d3
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 ION606
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18a911f9d3 | ||
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ION606
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414a4ac5a3 | ||
 ION606
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4eff5a6378 | ||
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ION606
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5adb4119f5 | ||
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ION606
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cd3ababd59 |
Vendored
+9
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{
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"[r]": {
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// generated automatically? What even....
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"editor.wordSeparators": "`~!@#$%^&*()-=+[{]}\\|;:'\",<>/",
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"editor.indentSize": "tabSize",
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"editor.useTabStops": true,
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}
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}
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##########################################
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### Principal Component Analysis (PCA) ###
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##########################################
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## load libraries
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library(ggplot2)
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library(ggfortify)
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library(GGally)
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library(e1071)
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library(class)
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library(psych)
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library(readr)
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## set working directory so that files can be referenced without the full path
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setwd("~/Courses/Data Analytics/Fall25/labs/lab 4/")
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## read dataset
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wine <- read_csv("wine.data", col_names = FALSE)
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## set column names
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names(wine) <- c("Type","Alcohol","Malic acid","Ash","Alcalinity of ash","Magnesium","Total phenols","Flavanoids","Nonflavanoid Phenols","Proanthocyanins","Color Intensity","Hue","Od280/od315 of diluted wines","Proline")
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## inspect data frame
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head(wine)
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## change the data type of the "Type" column from character to factor
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####
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# Factors look like regular strings (characters) but with factors R knows
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# that the column is a categorical variable with finite possible values
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# e.g. "Type" in the Wine dataset can only be 1, 2, or 3
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####
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wine$Type <- as.factor(wine$Type)
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## visualize variables
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pairs.panels(wine[,-1],gap = 0,bg = c("red", "yellow", "blue")[wine$Type],pch=21)
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ggpairs(wine, ggplot2::aes(colour = Type))
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###
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+128
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install.packages(
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c("e1071", "caret", "randomForest", "ggplot2", "pROC"),
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repos = c("https://cloud.r-project.org/"),
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dependencies = TRUE
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)
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suppressPackageStartupMessages({
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library(e1071) # for svm/tune.svm
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library(caret) # for metrics
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library(randomForest) # alternative classifier
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library(ggplot2)
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})
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set.seed(42)
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read_wine <- function() {
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df <- read.csv("wine.data", header = FALSE)
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colnames(df) <- c(
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"Class",
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"Alcohol", "Malic.acid", "Ash", "Alcalinity.of.ash", "Magnesium",
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"Total.phenols", "Flavanoids", "Nonflavanoid.phenols", "Proanthocyanins",
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"Color.intensity", "Hue", "OD280.OD315", "Proline"
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)
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df$Class <- factor(df$Class)
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df
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}
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df <- read_wine()
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# split into train/test
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idx <- createDataPartition(df$Class, p = 0.8, list = FALSE)
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train <- df[idx, ]
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test <- df[-idx, ]
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# choose a subset of features based on ANOVA F-test
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# I picked this sbuset before the runs:
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# alcohol, flavanoids, color intensity, od280/od315, proline, total phenols
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features <- c("Alcohol", "Flavanoids", "Color.intensity", "OD280.OD315", "Proline", "Total.phenols")
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x_train <- train[, features]
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y_train <- train$Class
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x_test <- test[, features]
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y_test <- test$Class
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# scale features
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pp <- preProcess(x_train, method = c("center", "scale"))
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x_train_s <- predict(pp, x_train)
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x_test_s <- predict(pp, x_test)
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# linear kernel svm with hyperparameter tuning (C)
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set.seed(42)
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lin_grid <- data.frame(cost = c(0.1, 1, 10, 100))
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tune_lin <- tune.svm(
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x = x_train_s, y = y_train,
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kernel = "linear",
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cost = lin_grid$cost,
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tunecontrol = tune.control(cross = 5)
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)
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lin_best <- tune_lin$best.model
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# rbf kernel svm with tuning (C, gamma)
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set.seed(42)
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rbf_grid_cost <- c(0.1, 1, 10, 100, 1000)
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rbf_grid_gamma <- c(0.001, 0.01, 0.1, 1)
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tune_rbf <- tune.svm(
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x = x_train_s, y = y_train,
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kernel = "radial",
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cost = rbf_grid_cost,
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gamma = rbf_grid_gamma,
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tunecontrol = tune.control(cross = 5)
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)
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rbf_best <- tune_rbf$best.model
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# alt classifier: random forest (same features)
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set.seed(42)
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rf_fit <- randomForest(x = x_train, y = y_train, ntree = 500, mtry = 2, importance = TRUE)
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# evaluation helper
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eval_model <- function(model, x_test_s, y_test, name) {
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pred <- predict(model, x_test_s)
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cm <- confusionMatrix(pred, y_test)
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pr <- data.frame(
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model = name,
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accuracy = cm$overall["Accuracy"],
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precision_macro = mean(cm$byClass[, "Precision"], na.rm = TRUE),
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recall_macro = mean(cm$byClass[, "Recall"], na.rm = TRUE),
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f1_macro = mean(cm$byClass[, "F1"], na.rm = TRUE)
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)
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list(cm = cm, pr = pr)
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}
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# eval svm models (use scaled features)
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lin_eval <- eval_model(lin_best, x_test_s, y_test, "svm_linear")
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rbf_eval <- eval_model(rbf_best, x_test_s, y_test, "svm_rbf")
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# evaluate random forest (no scaling)
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rf_pred <- predict(rf_fit, x_test)
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rf_cm <- confusionMatrix(rf_pred, y_test)
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rf_pr <- data.frame(
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model = "random_forest",
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accuracy = rf_cm$overall["Accuracy"],
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precision_macro = mean(rf_cm$byClass[, "Precision"], na.rm = TRUE),
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recall_macro = mean(rf_cm$byClass[, "Recall"], na.rm = TRUE),
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f1_macro = mean(rf_cm$byClass[, "F1"], na.rm = TRUE)
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)
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perf <- rbind(lin_eval$pr, rbf_eval$pr, rf_pr)
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# print
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cat("best params (linear svm): C =", lin_best$cost, "\n")
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cat("best params (rbf svm): C =", rbf_best$cost, " gamma =", rbf_best$gamma, "\n\n")
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print(perf)
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# macro-f1 comparison
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ggplot(perf, aes(x = model, y = f1_macro)) +
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geom_col() +
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labs(title = "macro-F1 by model (wine test set)")
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# save outputs
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write.table(perf, file = "lab5_performance_table.txt", sep = "\t", row.names = FALSE, quote = FALSE)
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sink("lab5_confusion_matrices.txt")
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cat("=== svm linear ===\n")
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print(lin_eval$cm)
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cat("\n=== svm rbf ===\n")
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print(rbf_eval$cm)
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cat("\n=== random forest ===\n")
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print(rf_cm)
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sink()
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@@ -0,0 +1,95 @@
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=== svm linear ===
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Confusion Matrix and Statistics
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Reference
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Prediction 1 2 3
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1 11 1 0
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2 0 13 0
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3 0 0 9
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Overall Statistics
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Accuracy : 0.9706
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95% CI : (0.8467, 0.9993)
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No Information Rate : 0.4118
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P-Value [Acc > NIR] : 3.92e-12
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Kappa : 0.9553
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Mcnemar's Test P-Value : NA
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Statistics by Class:
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Class: 1 Class: 2 Class: 3
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Sensitivity 1.0000 0.9286 1.0000
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Specificity 0.9565 1.0000 1.0000
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Pos Pred Value 0.9167 1.0000 1.0000
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Neg Pred Value 1.0000 0.9524 1.0000
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Prevalence 0.3235 0.4118 0.2647
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Detection Rate 0.3235 0.3824 0.2647
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Detection Prevalence 0.3529 0.3824 0.2647
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Balanced Accuracy 0.9783 0.9643 1.0000
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=== svm rbf ===
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Confusion Matrix and Statistics
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Reference
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Prediction 1 2 3
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1 11 1 0
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2 0 13 0
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3 0 0 9
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Overall Statistics
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Accuracy : 0.9706
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95% CI : (0.8467, 0.9993)
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No Information Rate : 0.4118
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P-Value [Acc > NIR] : 3.92e-12
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Kappa : 0.9553
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Mcnemar's Test P-Value : NA
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Statistics by Class:
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Class: 1 Class: 2 Class: 3
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Sensitivity 1.0000 0.9286 1.0000
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Specificity 0.9565 1.0000 1.0000
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Pos Pred Value 0.9167 1.0000 1.0000
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Neg Pred Value 1.0000 0.9524 1.0000
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Prevalence 0.3235 0.4118 0.2647
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Detection Rate 0.3235 0.3824 0.2647
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Detection Prevalence 0.3529 0.3824 0.2647
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Balanced Accuracy 0.9783 0.9643 1.0000
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=== random forest ===
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Confusion Matrix and Statistics
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Reference
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Prediction 1 2 3
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1 11 1 0
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2 0 13 0
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||||
3 0 0 9
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||||
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Overall Statistics
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||||
|
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Accuracy : 0.9706
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95% CI : (0.8467, 0.9993)
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No Information Rate : 0.4118
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P-Value [Acc > NIR] : 3.92e-12
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Kappa : 0.9553
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Mcnemar's Test P-Value : NA
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Statistics by Class:
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Class: 1 Class: 2 Class: 3
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Sensitivity 1.0000 0.9286 1.0000
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Specificity 0.9565 1.0000 1.0000
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Pos Pred Value 0.9167 1.0000 1.0000
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||||
Neg Pred Value 1.0000 0.9524 1.0000
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Prevalence 0.3235 0.4118 0.2647
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Detection Rate 0.3235 0.3824 0.2647
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Detection Prevalence 0.3529 0.3824 0.2647
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Balanced Accuracy 0.9783 0.9643 1.0000
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@@ -0,0 +1,4 @@
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model accuracy precision_macro recall_macro f1_macro
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svm_linear 0.970588235294118 0.972222222222222 0.976190476190476 0.973161567364466
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svm_rbf 0.970588235294118 0.972222222222222 0.976190476190476 0.973161567364466
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random_forest 0.970588235294118 0.972222222222222 0.976190476190476 0.973161567364466
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+178
@@ -0,0 +1,178 @@
|
||||
1,14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065
|
||||
1,13.2,1.78,2.14,11.2,100,2.65,2.76,.26,1.28,4.38,1.05,3.4,1050
|
||||
1,13.16,2.36,2.67,18.6,101,2.8,3.24,.3,2.81,5.68,1.03,3.17,1185
|
||||
1,14.37,1.95,2.5,16.8,113,3.85,3.49,.24,2.18,7.8,.86,3.45,1480
|
||||
1,13.24,2.59,2.87,21,118,2.8,2.69,.39,1.82,4.32,1.04,2.93,735
|
||||
1,14.2,1.76,2.45,15.2,112,3.27,3.39,.34,1.97,6.75,1.05,2.85,1450
|
||||
1,14.39,1.87,2.45,14.6,96,2.5,2.52,.3,1.98,5.25,1.02,3.58,1290
|
||||
1,14.06,2.15,2.61,17.6,121,2.6,2.51,.31,1.25,5.05,1.06,3.58,1295
|
||||
1,14.83,1.64,2.17,14,97,2.8,2.98,.29,1.98,5.2,1.08,2.85,1045
|
||||
1,13.86,1.35,2.27,16,98,2.98,3.15,.22,1.85,7.22,1.01,3.55,1045
|
||||
1,14.1,2.16,2.3,18,105,2.95,3.32,.22,2.38,5.75,1.25,3.17,1510
|
||||
1,14.12,1.48,2.32,16.8,95,2.2,2.43,.26,1.57,5,1.17,2.82,1280
|
||||
1,13.75,1.73,2.41,16,89,2.6,2.76,.29,1.81,5.6,1.15,2.9,1320
|
||||
1,14.75,1.73,2.39,11.4,91,3.1,3.69,.43,2.81,5.4,1.25,2.73,1150
|
||||
1,14.38,1.87,2.38,12,102,3.3,3.64,.29,2.96,7.5,1.2,3,1547
|
||||
1,13.63,1.81,2.7,17.2,112,2.85,2.91,.3,1.46,7.3,1.28,2.88,1310
|
||||
1,14.3,1.92,2.72,20,120,2.8,3.14,.33,1.97,6.2,1.07,2.65,1280
|
||||
1,13.83,1.57,2.62,20,115,2.95,3.4,.4,1.72,6.6,1.13,2.57,1130
|
||||
1,14.19,1.59,2.48,16.5,108,3.3,3.93,.32,1.86,8.7,1.23,2.82,1680
|
||||
1,13.64,3.1,2.56,15.2,116,2.7,3.03,.17,1.66,5.1,.96,3.36,845
|
||||
1,14.06,1.63,2.28,16,126,3,3.17,.24,2.1,5.65,1.09,3.71,780
|
||||
1,12.93,3.8,2.65,18.6,102,2.41,2.41,.25,1.98,4.5,1.03,3.52,770
|
||||
1,13.71,1.86,2.36,16.6,101,2.61,2.88,.27,1.69,3.8,1.11,4,1035
|
||||
1,12.85,1.6,2.52,17.8,95,2.48,2.37,.26,1.46,3.93,1.09,3.63,1015
|
||||
1,13.5,1.81,2.61,20,96,2.53,2.61,.28,1.66,3.52,1.12,3.82,845
|
||||
1,13.05,2.05,3.22,25,124,2.63,2.68,.47,1.92,3.58,1.13,3.2,830
|
||||
1,13.39,1.77,2.62,16.1,93,2.85,2.94,.34,1.45,4.8,.92,3.22,1195
|
||||
1,13.3,1.72,2.14,17,94,2.4,2.19,.27,1.35,3.95,1.02,2.77,1285
|
||||
1,13.87,1.9,2.8,19.4,107,2.95,2.97,.37,1.76,4.5,1.25,3.4,915
|
||||
1,14.02,1.68,2.21,16,96,2.65,2.33,.26,1.98,4.7,1.04,3.59,1035
|
||||
1,13.73,1.5,2.7,22.5,101,3,3.25,.29,2.38,5.7,1.19,2.71,1285
|
||||
1,13.58,1.66,2.36,19.1,106,2.86,3.19,.22,1.95,6.9,1.09,2.88,1515
|
||||
1,13.68,1.83,2.36,17.2,104,2.42,2.69,.42,1.97,3.84,1.23,2.87,990
|
||||
1,13.76,1.53,2.7,19.5,132,2.95,2.74,.5,1.35,5.4,1.25,3,1235
|
||||
1,13.51,1.8,2.65,19,110,2.35,2.53,.29,1.54,4.2,1.1,2.87,1095
|
||||
1,13.48,1.81,2.41,20.5,100,2.7,2.98,.26,1.86,5.1,1.04,3.47,920
|
||||
1,13.28,1.64,2.84,15.5,110,2.6,2.68,.34,1.36,4.6,1.09,2.78,880
|
||||
1,13.05,1.65,2.55,18,98,2.45,2.43,.29,1.44,4.25,1.12,2.51,1105
|
||||
1,13.07,1.5,2.1,15.5,98,2.4,2.64,.28,1.37,3.7,1.18,2.69,1020
|
||||
1,14.22,3.99,2.51,13.2,128,3,3.04,.2,2.08,5.1,.89,3.53,760
|
||||
1,13.56,1.71,2.31,16.2,117,3.15,3.29,.34,2.34,6.13,.95,3.38,795
|
||||
1,13.41,3.84,2.12,18.8,90,2.45,2.68,.27,1.48,4.28,.91,3,1035
|
||||
1,13.88,1.89,2.59,15,101,3.25,3.56,.17,1.7,5.43,.88,3.56,1095
|
||||
1,13.24,3.98,2.29,17.5,103,2.64,2.63,.32,1.66,4.36,.82,3,680
|
||||
1,13.05,1.77,2.1,17,107,3,3,.28,2.03,5.04,.88,3.35,885
|
||||
1,14.21,4.04,2.44,18.9,111,2.85,2.65,.3,1.25,5.24,.87,3.33,1080
|
||||
1,14.38,3.59,2.28,16,102,3.25,3.17,.27,2.19,4.9,1.04,3.44,1065
|
||||
1,13.9,1.68,2.12,16,101,3.1,3.39,.21,2.14,6.1,.91,3.33,985
|
||||
1,14.1,2.02,2.4,18.8,103,2.75,2.92,.32,2.38,6.2,1.07,2.75,1060
|
||||
1,13.94,1.73,2.27,17.4,108,2.88,3.54,.32,2.08,8.90,1.12,3.1,1260
|
||||
1,13.05,1.73,2.04,12.4,92,2.72,3.27,.17,2.91,7.2,1.12,2.91,1150
|
||||
1,13.83,1.65,2.6,17.2,94,2.45,2.99,.22,2.29,5.6,1.24,3.37,1265
|
||||
1,13.82,1.75,2.42,14,111,3.88,3.74,.32,1.87,7.05,1.01,3.26,1190
|
||||
1,13.77,1.9,2.68,17.1,115,3,2.79,.39,1.68,6.3,1.13,2.93,1375
|
||||
1,13.74,1.67,2.25,16.4,118,2.6,2.9,.21,1.62,5.85,.92,3.2,1060
|
||||
1,13.56,1.73,2.46,20.5,116,2.96,2.78,.2,2.45,6.25,.98,3.03,1120
|
||||
1,14.22,1.7,2.3,16.3,118,3.2,3,.26,2.03,6.38,.94,3.31,970
|
||||
1,13.29,1.97,2.68,16.8,102,3,3.23,.31,1.66,6,1.07,2.84,1270
|
||||
1,13.72,1.43,2.5,16.7,108,3.4,3.67,.19,2.04,6.8,.89,2.87,1285
|
||||
2,12.37,.94,1.36,10.6,88,1.98,.57,.28,.42,1.95,1.05,1.82,520
|
||||
2,12.33,1.1,2.28,16,101,2.05,1.09,.63,.41,3.27,1.25,1.67,680
|
||||
2,12.64,1.36,2.02,16.8,100,2.02,1.41,.53,.62,5.75,.98,1.59,450
|
||||
2,13.67,1.25,1.92,18,94,2.1,1.79,.32,.73,3.8,1.23,2.46,630
|
||||
2,12.37,1.13,2.16,19,87,3.5,3.1,.19,1.87,4.45,1.22,2.87,420
|
||||
2,12.17,1.45,2.53,19,104,1.89,1.75,.45,1.03,2.95,1.45,2.23,355
|
||||
2,12.37,1.21,2.56,18.1,98,2.42,2.65,.37,2.08,4.6,1.19,2.3,678
|
||||
2,13.11,1.01,1.7,15,78,2.98,3.18,.26,2.28,5.3,1.12,3.18,502
|
||||
2,12.37,1.17,1.92,19.6,78,2.11,2,.27,1.04,4.68,1.12,3.48,510
|
||||
2,13.34,.94,2.36,17,110,2.53,1.3,.55,.42,3.17,1.02,1.93,750
|
||||
2,12.21,1.19,1.75,16.8,151,1.85,1.28,.14,2.5,2.85,1.28,3.07,718
|
||||
2,12.29,1.61,2.21,20.4,103,1.1,1.02,.37,1.46,3.05,.906,1.82,870
|
||||
2,13.86,1.51,2.67,25,86,2.95,2.86,.21,1.87,3.38,1.36,3.16,410
|
||||
2,13.49,1.66,2.24,24,87,1.88,1.84,.27,1.03,3.74,.98,2.78,472
|
||||
2,12.99,1.67,2.6,30,139,3.3,2.89,.21,1.96,3.35,1.31,3.5,985
|
||||
2,11.96,1.09,2.3,21,101,3.38,2.14,.13,1.65,3.21,.99,3.13,886
|
||||
2,11.66,1.88,1.92,16,97,1.61,1.57,.34,1.15,3.8,1.23,2.14,428
|
||||
2,13.03,.9,1.71,16,86,1.95,2.03,.24,1.46,4.6,1.19,2.48,392
|
||||
2,11.84,2.89,2.23,18,112,1.72,1.32,.43,.95,2.65,.96,2.52,500
|
||||
2,12.33,.99,1.95,14.8,136,1.9,1.85,.35,2.76,3.4,1.06,2.31,750
|
||||
2,12.7,3.87,2.4,23,101,2.83,2.55,.43,1.95,2.57,1.19,3.13,463
|
||||
2,12,.92,2,19,86,2.42,2.26,.3,1.43,2.5,1.38,3.12,278
|
||||
2,12.72,1.81,2.2,18.8,86,2.2,2.53,.26,1.77,3.9,1.16,3.14,714
|
||||
2,12.08,1.13,2.51,24,78,2,1.58,.4,1.4,2.2,1.31,2.72,630
|
||||
2,13.05,3.86,2.32,22.5,85,1.65,1.59,.61,1.62,4.8,.84,2.01,515
|
||||
2,11.84,.89,2.58,18,94,2.2,2.21,.22,2.35,3.05,.79,3.08,520
|
||||
2,12.67,.98,2.24,18,99,2.2,1.94,.3,1.46,2.62,1.23,3.16,450
|
||||
2,12.16,1.61,2.31,22.8,90,1.78,1.69,.43,1.56,2.45,1.33,2.26,495
|
||||
2,11.65,1.67,2.62,26,88,1.92,1.61,.4,1.34,2.6,1.36,3.21,562
|
||||
2,11.64,2.06,2.46,21.6,84,1.95,1.69,.48,1.35,2.8,1,2.75,680
|
||||
2,12.08,1.33,2.3,23.6,70,2.2,1.59,.42,1.38,1.74,1.07,3.21,625
|
||||
2,12.08,1.83,2.32,18.5,81,1.6,1.5,.52,1.64,2.4,1.08,2.27,480
|
||||
2,12,1.51,2.42,22,86,1.45,1.25,.5,1.63,3.6,1.05,2.65,450
|
||||
2,12.69,1.53,2.26,20.7,80,1.38,1.46,.58,1.62,3.05,.96,2.06,495
|
||||
2,12.29,2.83,2.22,18,88,2.45,2.25,.25,1.99,2.15,1.15,3.3,290
|
||||
2,11.62,1.99,2.28,18,98,3.02,2.26,.17,1.35,3.25,1.16,2.96,345
|
||||
2,12.47,1.52,2.2,19,162,2.5,2.27,.32,3.28,2.6,1.16,2.63,937
|
||||
2,11.81,2.12,2.74,21.5,134,1.6,.99,.14,1.56,2.5,.95,2.26,625
|
||||
2,12.29,1.41,1.98,16,85,2.55,2.5,.29,1.77,2.9,1.23,2.74,428
|
||||
2,12.37,1.07,2.1,18.5,88,3.52,3.75,.24,1.95,4.5,1.04,2.77,660
|
||||
2,12.29,3.17,2.21,18,88,2.85,2.99,.45,2.81,2.3,1.42,2.83,406
|
||||
2,12.08,2.08,1.7,17.5,97,2.23,2.17,.26,1.4,3.3,1.27,2.96,710
|
||||
2,12.6,1.34,1.9,18.5,88,1.45,1.36,.29,1.35,2.45,1.04,2.77,562
|
||||
2,12.34,2.45,2.46,21,98,2.56,2.11,.34,1.31,2.8,.8,3.38,438
|
||||
2,11.82,1.72,1.88,19.5,86,2.5,1.64,.37,1.42,2.06,.94,2.44,415
|
||||
2,12.51,1.73,1.98,20.5,85,2.2,1.92,.32,1.48,2.94,1.04,3.57,672
|
||||
2,12.42,2.55,2.27,22,90,1.68,1.84,.66,1.42,2.7,.86,3.3,315
|
||||
2,12.25,1.73,2.12,19,80,1.65,2.03,.37,1.63,3.4,1,3.17,510
|
||||
2,12.72,1.75,2.28,22.5,84,1.38,1.76,.48,1.63,3.3,.88,2.42,488
|
||||
2,12.22,1.29,1.94,19,92,2.36,2.04,.39,2.08,2.7,.86,3.02,312
|
||||
2,11.61,1.35,2.7,20,94,2.74,2.92,.29,2.49,2.65,.96,3.26,680
|
||||
2,11.46,3.74,1.82,19.5,107,3.18,2.58,.24,3.58,2.9,.75,2.81,562
|
||||
2,12.52,2.43,2.17,21,88,2.55,2.27,.26,1.22,2,.9,2.78,325
|
||||
2,11.76,2.68,2.92,20,103,1.75,2.03,.6,1.05,3.8,1.23,2.5,607
|
||||
2,11.41,.74,2.5,21,88,2.48,2.01,.42,1.44,3.08,1.1,2.31,434
|
||||
2,12.08,1.39,2.5,22.5,84,2.56,2.29,.43,1.04,2.9,.93,3.19,385
|
||||
2,11.03,1.51,2.2,21.5,85,2.46,2.17,.52,2.01,1.9,1.71,2.87,407
|
||||
2,11.82,1.47,1.99,20.8,86,1.98,1.6,.3,1.53,1.95,.95,3.33,495
|
||||
2,12.42,1.61,2.19,22.5,108,2,2.09,.34,1.61,2.06,1.06,2.96,345
|
||||
2,12.77,3.43,1.98,16,80,1.63,1.25,.43,.83,3.4,.7,2.12,372
|
||||
2,12,3.43,2,19,87,2,1.64,.37,1.87,1.28,.93,3.05,564
|
||||
2,11.45,2.4,2.42,20,96,2.9,2.79,.32,1.83,3.25,.8,3.39,625
|
||||
2,11.56,2.05,3.23,28.5,119,3.18,5.08,.47,1.87,6,.93,3.69,465
|
||||
2,12.42,4.43,2.73,26.5,102,2.2,2.13,.43,1.71,2.08,.92,3.12,365
|
||||
2,13.05,5.8,2.13,21.5,86,2.62,2.65,.3,2.01,2.6,.73,3.1,380
|
||||
2,11.87,4.31,2.39,21,82,2.86,3.03,.21,2.91,2.8,.75,3.64,380
|
||||
2,12.07,2.16,2.17,21,85,2.6,2.65,.37,1.35,2.76,.86,3.28,378
|
||||
2,12.43,1.53,2.29,21.5,86,2.74,3.15,.39,1.77,3.94,.69,2.84,352
|
||||
2,11.79,2.13,2.78,28.5,92,2.13,2.24,.58,1.76,3,.97,2.44,466
|
||||
2,12.37,1.63,2.3,24.5,88,2.22,2.45,.4,1.9,2.12,.89,2.78,342
|
||||
2,12.04,4.3,2.38,22,80,2.1,1.75,.42,1.35,2.6,.79,2.57,580
|
||||
3,12.86,1.35,2.32,18,122,1.51,1.25,.21,.94,4.1,.76,1.29,630
|
||||
3,12.88,2.99,2.4,20,104,1.3,1.22,.24,.83,5.4,.74,1.42,530
|
||||
3,12.81,2.31,2.4,24,98,1.15,1.09,.27,.83,5.7,.66,1.36,560
|
||||
3,12.7,3.55,2.36,21.5,106,1.7,1.2,.17,.84,5,.78,1.29,600
|
||||
3,12.51,1.24,2.25,17.5,85,2,.58,.6,1.25,5.45,.75,1.51,650
|
||||
3,12.6,2.46,2.2,18.5,94,1.62,.66,.63,.94,7.1,.73,1.58,695
|
||||
3,12.25,4.72,2.54,21,89,1.38,.47,.53,.8,3.85,.75,1.27,720
|
||||
3,12.53,5.51,2.64,25,96,1.79,.6,.63,1.1,5,.82,1.69,515
|
||||
3,13.49,3.59,2.19,19.5,88,1.62,.48,.58,.88,5.7,.81,1.82,580
|
||||
3,12.84,2.96,2.61,24,101,2.32,.6,.53,.81,4.92,.89,2.15,590
|
||||
3,12.93,2.81,2.7,21,96,1.54,.5,.53,.75,4.6,.77,2.31,600
|
||||
3,13.36,2.56,2.35,20,89,1.4,.5,.37,.64,5.6,.7,2.47,780
|
||||
3,13.52,3.17,2.72,23.5,97,1.55,.52,.5,.55,4.35,.89,2.06,520
|
||||
3,13.62,4.95,2.35,20,92,2,.8,.47,1.02,4.4,.91,2.05,550
|
||||
3,12.25,3.88,2.2,18.5,112,1.38,.78,.29,1.14,8.21,.65,2,855
|
||||
3,13.16,3.57,2.15,21,102,1.5,.55,.43,1.3,4,.6,1.68,830
|
||||
3,13.88,5.04,2.23,20,80,.98,.34,.4,.68,4.9,.58,1.33,415
|
||||
3,12.87,4.61,2.48,21.5,86,1.7,.65,.47,.86,7.65,.54,1.86,625
|
||||
3,13.32,3.24,2.38,21.5,92,1.93,.76,.45,1.25,8.42,.55,1.62,650
|
||||
3,13.08,3.9,2.36,21.5,113,1.41,1.39,.34,1.14,9.40,.57,1.33,550
|
||||
3,13.5,3.12,2.62,24,123,1.4,1.57,.22,1.25,8.60,.59,1.3,500
|
||||
3,12.79,2.67,2.48,22,112,1.48,1.36,.24,1.26,10.8,.48,1.47,480
|
||||
3,13.11,1.9,2.75,25.5,116,2.2,1.28,.26,1.56,7.1,.61,1.33,425
|
||||
3,13.23,3.3,2.28,18.5,98,1.8,.83,.61,1.87,10.52,.56,1.51,675
|
||||
3,12.58,1.29,2.1,20,103,1.48,.58,.53,1.4,7.6,.58,1.55,640
|
||||
3,13.17,5.19,2.32,22,93,1.74,.63,.61,1.55,7.9,.6,1.48,725
|
||||
3,13.84,4.12,2.38,19.5,89,1.8,.83,.48,1.56,9.01,.57,1.64,480
|
||||
3,12.45,3.03,2.64,27,97,1.9,.58,.63,1.14,7.5,.67,1.73,880
|
||||
3,14.34,1.68,2.7,25,98,2.8,1.31,.53,2.7,13,.57,1.96,660
|
||||
3,13.48,1.67,2.64,22.5,89,2.6,1.1,.52,2.29,11.75,.57,1.78,620
|
||||
3,12.36,3.83,2.38,21,88,2.3,.92,.5,1.04,7.65,.56,1.58,520
|
||||
3,13.69,3.26,2.54,20,107,1.83,.56,.5,.8,5.88,.96,1.82,680
|
||||
3,12.85,3.27,2.58,22,106,1.65,.6,.6,.96,5.58,.87,2.11,570
|
||||
3,12.96,3.45,2.35,18.5,106,1.39,.7,.4,.94,5.28,.68,1.75,675
|
||||
3,13.78,2.76,2.3,22,90,1.35,.68,.41,1.03,9.58,.7,1.68,615
|
||||
3,13.73,4.36,2.26,22.5,88,1.28,.47,.52,1.15,6.62,.78,1.75,520
|
||||
3,13.45,3.7,2.6,23,111,1.7,.92,.43,1.46,10.68,.85,1.56,695
|
||||
3,12.82,3.37,2.3,19.5,88,1.48,.66,.4,.97,10.26,.72,1.75,685
|
||||
3,13.58,2.58,2.69,24.5,105,1.55,.84,.39,1.54,8.66,.74,1.8,750
|
||||
3,13.4,4.6,2.86,25,112,1.98,.96,.27,1.11,8.5,.67,1.92,630
|
||||
3,12.2,3.03,2.32,19,96,1.25,.49,.4,.73,5.5,.66,1.83,510
|
||||
3,12.77,2.39,2.28,19.5,86,1.39,.51,.48,.64,9.899999,.57,1.63,470
|
||||
3,14.16,2.51,2.48,20,91,1.68,.7,.44,1.24,9.7,.62,1.71,660
|
||||
3,13.71,5.65,2.45,20.5,95,1.68,.61,.52,1.06,7.7,.64,1.74,740
|
||||
3,13.4,3.91,2.48,23,102,1.8,.75,.43,1.41,7.3,.7,1.56,750
|
||||
3,13.27,4.28,2.26,20,120,1.59,.69,.43,1.35,10.2,.59,1.56,835
|
||||
3,13.17,2.59,2.37,20,120,1.65,.68,.53,1.46,9.3,.6,1.62,840
|
||||
3,14.13,4.1,2.74,24.5,96,2.05,.76,.56,1.35,9.2,.61,1.6,560
|
||||
@@ -0,0 +1,100 @@
|
||||
1. Title of Database: Wine recognition data
|
||||
Updated Sept 21, 1998 by C.Blake : Added attribute information
|
||||
|
||||
2. Sources:
|
||||
(a) Forina, M. et al, PARVUS - An Extendible Package for Data
|
||||
Exploration, Classification and Correlation. Institute of Pharmaceutical
|
||||
and Food Analysis and Technologies, Via Brigata Salerno,
|
||||
16147 Genoa, Italy.
|
||||
|
||||
(b) Stefan Aeberhard, email: stefan@coral.cs.jcu.edu.au
|
||||
(c) July 1991
|
||||
3. Past Usage:
|
||||
|
||||
(1)
|
||||
S. Aeberhard, D. Coomans and O. de Vel,
|
||||
Comparison of Classifiers in High Dimensional Settings,
|
||||
Tech. Rep. no. 92-02, (1992), Dept. of Computer Science and Dept. of
|
||||
Mathematics and Statistics, James Cook University of North Queensland.
|
||||
(Also submitted to Technometrics).
|
||||
|
||||
The data was used with many others for comparing various
|
||||
classifiers. The classes are separable, though only RDA
|
||||
has achieved 100% correct classification.
|
||||
(RDA : 100%, QDA 99.4%, LDA 98.9%, 1NN 96.1% (z-transformed data))
|
||||
(All results using the leave-one-out technique)
|
||||
|
||||
In a classification context, this is a well posed problem
|
||||
with "well behaved" class structures. A good data set
|
||||
for first testing of a new classifier, but not very
|
||||
challenging.
|
||||
|
||||
(2)
|
||||
S. Aeberhard, D. Coomans and O. de Vel,
|
||||
"THE CLASSIFICATION PERFORMANCE OF RDA"
|
||||
Tech. Rep. no. 92-01, (1992), Dept. of Computer Science and Dept. of
|
||||
Mathematics and Statistics, James Cook University of North Queensland.
|
||||
(Also submitted to Journal of Chemometrics).
|
||||
|
||||
Here, the data was used to illustrate the superior performance of
|
||||
the use of a new appreciation function with RDA.
|
||||
|
||||
4. Relevant Information:
|
||||
|
||||
-- These data are the results of a chemical analysis of
|
||||
wines grown in the same region in Italy but derived from three
|
||||
different cultivars.
|
||||
The analysis determined the quantities of 13 constituents
|
||||
found in each of the three types of wines.
|
||||
|
||||
-- I think that the initial data set had around 30 variables, but
|
||||
for some reason I only have the 13 dimensional version.
|
||||
I had a list of what the 30 or so variables were, but a.)
|
||||
I lost it, and b.), I would not know which 13 variables
|
||||
are included in the set.
|
||||
|
||||
-- The attributes are (dontated by Riccardo Leardi,
|
||||
riclea@anchem.unige.it )
|
||||
1) Alcohol
|
||||
2) Malic acid
|
||||
3) Ash
|
||||
4) Alcalinity of ash
|
||||
5) Magnesium
|
||||
6) Total phenols
|
||||
7) Flavanoids
|
||||
8) Nonflavanoid phenols
|
||||
9) Proanthocyanins
|
||||
10)Color intensity
|
||||
11)Hue
|
||||
12)OD280/OD315 of diluted wines
|
||||
13)Proline
|
||||
|
||||
5. Number of Instances
|
||||
|
||||
class 1 59
|
||||
class 2 71
|
||||
class 3 48
|
||||
|
||||
6. Number of Attributes
|
||||
|
||||
13
|
||||
|
||||
7. For Each Attribute:
|
||||
|
||||
All attributes are continuous
|
||||
|
||||
No statistics available, but suggest to standardise
|
||||
variables for certain uses (e.g. for us with classifiers
|
||||
which are NOT scale invariant)
|
||||
|
||||
NOTE: 1st attribute is class identifier (1-3)
|
||||
|
||||
8. Missing Attribute Values:
|
||||
|
||||
None
|
||||
|
||||
9. Class Distribution: number of instances per class
|
||||
|
||||
class 1 59
|
||||
class 2 71
|
||||
class 3 48
|
||||
Binary file not shown.
Reference in New Issue
Block a user