調整線性 SVM

R 的支援向量機

Kailash Awati

Instructor

線性 SVM，預設 cost

library(e1071)
svm_model <- svm(y ~ ., 
                data = trainset, 
                type = "C-classification", 
                kernel = "linear", 
                scale = FALSE)
# Print model summary
svm_model

Call:
svm(formula = y ~ .,
    data = trainset,
    type = "C-classification", 
    kernel = "linear",
    scale = FALSE)

Parameters:
SVM-Type:  C-classification 
 SVM-Kernel:  linear 
       cost:  1 
      gamma:  0.5 
Number of Support Vectors:  55

第 2.3 章－線性可分資料集，線性核心預設 cost、支持向量、決策邊界與間隔

線性 SVM（cost = 100）

library(e1071)
svm_model <- svm(y ~ ., 
                data = trainset, 
                type = "C-classification", 
                kernel = "linear", 
                cost = 100,
                scale = FALSE)
# Print model summary
svm_model

Call:
svm(formula = y ~ .,
    data = trainset,
    type = "C-classification", 
    kernel = "linear",
    cost = 100,
    scale = FALSE)

Parameters:
SVM-Type:  C-classification 
 SVM-Kernel:  linear 
       cost:  100 
      gamma:  0.5 
Number of Support Vectors:  6

第 2.3 章－線性可分資料，線性核心 cost = 100、支持向量、決策邊界與間隔

啟示

若已知決策邊界為線性，縮小間隔可能有用
但在實務上很少如此

第 2.3 章－非線性可分資料集

非線性資料集，線性 SVM（cost = 100）

用 80% 資料的訓練集建立 cost=100 的模型

# Build model
library(e1071)
svm_model<- svm(y ~ ., 
                data = trainset, 
                type = "C-classification", 
                kernel = "linear", 
                cost = 100,
                scale = FALSE)

計算準確率

# Train and test accuracy
pred_train <- predict(svm_model, trainset)
mean(pred_train == trainset$y)

0.8208333

pred_test <- predict(svm_model, testset)
mean(pred_test == testset$y)

0.85

50 次隨機分割的平均測試準確率：82.9%

第 2.3 章－非線性可分資料集，線性核心 cost=100 的支持向量、決策邊界與間隔

非線性資料集，線性 SVM（cost = 1）

重新建立模型，設定 cost = 1

# Trainset contains 80% of data
# Same train/test split as before.
# Build model
svm_model <- svm(y ~ ., 
                data = trainset, 
                type = "C-classification", 
                kernel = "linear", 
                cost = 1,
                scale = FALSE)

計算測試準確率

# Test accuracy
pred_test <- predict(svm_model, testset)
mean(pred_test == testset$y)

0.8666667

50 次隨機分割的平均測試準確率：83.7%

第 2.3 章－非線性可分資料集，線性核心 cost=1 的支持向量、決策邊界與間隔

一起來練習吧！

R 的支援向量機