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锟斤拷锟缴拷锟絉锟斤拷锟斤拷预锟解工锟斤拷

锟斤拷源锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷|锟斤拷时锟戒：2024-07-01 11:56:46 |锟斤拷锟侥讹拷锟斤拷145锟斤拷|锟斤拷 锟斤拷签锟斤拷 森锟斤拷 锟斤拷 |锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷

锟斤拷锟斤拷锟斤拷细锟斤拷锟斤拷锟斤拷R锟斤拷锟皆斤拷锟斤拷预锟斤拷拇锟斤拷锟绞撅拷锟斤拷锟斤拷约锟斤拷锟斤拷森锟斤拷R锟斤拷锟皆碉拷应锟斤拷实锟斤拷锟斤拷同时锟斤拷细锟斤拷锟斤拷锟斤拷锟斤拷锟缴拷值锟接︼拷锟绞碉拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟较革拷拇锟斤拷锟绞撅拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷猓拷苫锟斤拷锟斤拷锟斤拷锟�

锟斤拷锟缴拷郑锟絉andom Forest锟斤拷锟斤拷一锟街伙拷锟节撅拷锟斤拷锟斤拷锟侥硷拷锟斤拷学习锟斤拷锟斤拷锟斤拷锟斤拷通锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷堑锟皆わ拷锟斤拷锟斤拷锟斤拷锟斤拷预锟斤拷锟阶既凤拷浴锟斤拷锟絉锟斤拷锟斤拷锟叫ｏ拷锟斤拷锟角匡拷锟斤拷使锟斤拷 randomForest 锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷训锟斤拷锟斤拷锟缴拷锟侥ｏ拷汀锟斤拷锟斤拷锟斤拷嵌锟斤拷锟斤拷森锟街碉拷锟斤拷细锟斤拷锟斤拷锟皆硷拷使锟斤拷R锟斤拷锟皆斤拷锟斤拷预锟斤拷拇锟斤拷锟绞撅拷锟斤拷锟�

1. R锟斤拷锟皆斤拷锟斤拷预锟斤拷拇锟斤拷锟绞撅拷锟�

1.1 锟斤拷锟缴拷旨锟斤拷

锟斤拷锟缴拷锟酵拷锟斤拷锟斤拷虏锟斤拷锟斤拷锟叫癸拷锟斤拷锟斤拷

锟斤拷1锟斤拷 锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷Bootstrap Sampling锟斤拷 锟斤拷锟斤拷原始锟斤拷锟捷硷拷锟斤拷锟叫放回碉拷锟斤拷锟斤拷锟饺★拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷训锟斤拷锟斤拷镁锟斤拷锟斤拷锟斤拷锟�

锟斤拷2锟斤拷 锟斤拷锟斤拷锟斤拷锟窖★拷锟� 锟斤拷锟斤拷训锟斤拷每锟矫撅拷锟斤拷锟斤拷时锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟窖★拷锟揭伙拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷薪诘锟斤拷锟窖★拷

锟斤拷3锟斤拷 锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷 锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟矫碉拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟窖★拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷镁锟斤拷锟斤拷锟斤拷锟�

锟斤拷4锟斤拷 锟斤拷锟斤拷预锟斤拷 锟斤拷锟斤拷锟节凤拷锟斤拷锟斤拷锟解，通锟斤拷投票锟斤拷锟斤拷锟斤拷锟斤拷投票锟斤拷锟斤拷锟斤拷锟斤拷锟叫撅拷锟斤拷锟斤拷锟斤拷预锟斤拷锟斤拷锟斤拷锟斤拷锟节回癸拷锟斤拷锟解，通锟斤拷取平锟斤拷值锟斤拷锟斤拷锟斤拷锟叫撅拷锟斤拷锟斤拷锟斤拷预锟斤拷锟斤拷锟斤拷

锟斤拷锟缴拷值锟斤拷诺锟斤拷锟斤拷锟斤拷

  • 锟斤拷锟皆达拷锟斤拷锟斤拷维锟斤拷锟捷ｏ拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟窖★拷锟�
  • 锟杰癸拷学习锟斤拷锟斤拷之锟斤拷锟斤拷嗷ビ帮拷欤拷也锟斤拷锟斤拷坠锟斤拷锟较★拷
  • 锟斤拷锟节诧拷平锟斤拷锟斤拷锟斤拷菁锟斤拷锟斤拷锟斤拷锟狡斤拷锟斤拷锟筋。
  • 锟斤拷鹊锟揭伙拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷懈锟斤拷叩锟皆わ拷锟阶既凤拷浴锟�

1.2 R锟斤拷锟皆达拷锟斤拷示锟斤拷

锟斤拷锟斤拷锟斤拷一锟斤拷使锟斤拷R锟斤拷锟斤拷锟叫碉拷 randomForest 锟斤拷锟斤拷锟斤拷锟斤拷锟缴拷锟皆わ拷锟侥达拷锟斤拷示锟斤拷锟斤拷 

# 锟斤拷装randomForest锟斤拷锟斤拷锟斤拷锟斤拷锟轿达拷锟阶帮拷锟�  
install.packages("randomForest")  
  
# 锟斤拷锟斤拷randomForest锟斤拷  
library(randomForest)  
  
# 锟斤拷锟斤拷锟斤拷锟捷硷拷锟斤拷锟斤拷锟斤拷锟斤拷iris锟斤拷锟捷硷拷为锟斤拷锟斤拷  
data(iris)  
  
# 锟斤拷锟斤拷训锟斤拷锟斤拷锟酵诧拷锟皆硷拷  
set.seed(123) # 锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷员锟街わ拷锟斤拷锟侥匡拷锟截革拷锟斤拷  
train_index <- sample(1:nrow(iris), nrow(iris)*0.7) # 锟斤拷锟窖★拷锟�70%锟斤拷锟斤拷锟斤拷锟斤拷为训锟斤拷锟斤拷  
train_data <- iris[train_index,]  
test_data <- iris[-train_index,]  
  
# 使锟斤拷randomForest锟斤拷锟斤拷训锟斤拷锟斤拷锟缴拷锟侥ｏ拷锟�  
# ntree指锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷mtry指锟斤拷每锟轿凤拷锟斤拷时锟斤拷锟窖★拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷  
model <- randomForest(Species ~ ., data=train_data, ntree=500, mtry=2)  
  
# 使锟斤拷训锟斤拷锟矫碉拷模锟酵对诧拷锟皆硷拷锟斤拷锟斤拷预锟斤拷  
predictions <- predict(model, newdata=test_data)  
  
# 锟斤拷锟斤拷模锟斤拷锟斤拷锟斤拷  
# 锟斤拷锟节凤拷锟斤拷锟斤拷锟解，锟斤拷锟皆硷拷锟斤拷准确锟绞★拷锟斤拷锟斤拷锟斤拷锟斤拷锟街革拷锟�  
confusionMatrix <- table(predictions, test_data$Species)  
accuracy <- sum(diag(confusionMatrix)) / sum(confusionMatrix)  
print(paste("Accuracy:", accuracy))  
  
# 锟斤拷锟斤拷锟揭拷锟斤拷锟斤拷锟斤拷曰锟斤拷锟斤拷锟斤拷锟斤拷锟揭拷锟酵�  
# importance(model) # 锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷要锟皆撅拷锟斤拷  
# plot(importance(model)) # 锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷要锟斤拷图

1.3 实锟斤拷应锟斤拷锟斤拷锟斤拷

锟斤拷锟缴拷锟斤拷锟绞碉拷锟接︼拷锟斤拷芯锟斤拷泄惴猴拷锟斤拷锟斤拷澹拷乇锟斤拷锟斤拷诖锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷菁锟斤拷徒锟斤拷锟皆わ拷锟斤拷锟斤拷时锟斤拷锟斤拷锟界，锟斤拷锟斤拷锟斤拷锟斤拷息学锟斤拷医学锟斤拷稀锟斤拷锟斤拷锟皆わ拷锟斤拷锟斤拷锟斤拷锟斤拷锟缴拷挚锟斤拷锟斤拷锟斤拷诜锟斤拷唷拷毓椤拷锟斤拷锟窖★拷锟斤拷锟斤拷锟解。通锟斤拷锟斤拷锟缴讹拷镁锟斤拷锟斤拷锟斤拷锟皆わ拷锟斤拷锟斤拷锟斤拷锟斤拷森锟街匡拷锟斤拷锟斤拷锟皆わ拷锟斤拷准确锟皆ｏ拷锟斤拷锟斤拷锟酵癸拷锟斤拷系姆锟斤拷铡锟斤拷锟斤拷猓拷锟斤拷森锟街伙拷锟斤拷锟斤拷锟结供锟斤拷锟斤拷锟斤拷要锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷些锟斤拷锟斤拷锟斤拷预锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷要影锟届。

2. 锟斤拷锟缴拷锟絉锟斤拷锟斤拷应锟斤拷实锟斤拷

锟斤拷谈锟斤拷锟斤拷锟缴拷值锟接︼拷锟绞碉拷锟绞憋拷锟斤拷锟斤拷锟斤拷锟揭恍╋拷锟斤拷锟侥筹拷锟斤拷锟皆硷拷锟斤拷锟绞癸拷锟絉锟斤拷锟斤拷锟叫碉拷 randomForest 锟斤拷锟斤拷实锟斤拷锟斤拷些实锟斤拷锟斤拷锟斤拷细锟斤拷锟斤拷示锟斤拷锟斤拷

2.1 锟斤拷锟斤拷锟斤拷希锟斤拷锟斤拷锟斤拷侔锟斤拷锟斤拷为锟斤拷锟斤拷

2.1.1 锟斤拷锟捷硷拷锟斤拷锟斤拷锟劫帮拷锟斤拷锟捷硷拷锟斤拷 breastCancer 锟斤拷

锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷一锟斤拷锟斤拷锟劫帮拷锟斤拷锟捷硷拷锟斤拷锟斤拷锟叫帮拷锟斤拷一些锟诫癌症锟斤拷氐锟斤拷锟斤拷锟斤拷锟揭伙拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷欠锟轿拷锟斤拷裕锟斤拷锟斤拷锟斤拷堑锟侥匡拷锟斤拷锟窖碉拷锟揭伙拷锟斤拷锟斤拷森锟斤拷模锟斤拷锟斤拷预锟斤拷锟铰的诧拷锟斤拷锟角凤拷为锟斤拷锟皆★拷

2.1.2 锟斤拷锟斤拷示锟斤拷 

# 锟斤拷锟截憋拷要锟侥帮拷  
library(randomForest)  
  
# 锟斤拷锟斤拷锟斤拷锟捷硷拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷丫锟斤拷锟斤拷锟絙reastCancer锟斤拷锟捷硷拷锟斤拷  
# 锟斤拷锟斤拷锟揭拷锟斤拷锟斤拷源锟斤拷獠匡拷锟斤拷锟皆达拷锟斤拷兀锟斤拷锟絩ead.csv  
data(breastCancer, package = "mlbench") # 锟斤拷锟斤拷breastCancer锟斤拷mlbench锟斤拷锟斤拷  
  
# 锟斤拷锟斤拷训锟斤拷锟斤拷锟酵诧拷锟皆硷拷  
set.seed(123) # 为锟剿斤拷锟斤拷目筛锟斤拷锟斤拷锟�  
trainIndex <- sample(1:nrow(breastCancer), nrow(breastCancer)*0.7)  
trainData <- breastCancer[trainIndex, ]  
testData <- breastCancer[-trainIndex, ]  
  
# 使锟斤拷锟斤拷锟缴拷锟侥ｏ拷徒锟斤拷锟窖碉拷锟�  
rfModel <- randomForest(Class ~ ., data = trainData, ntree = 500, importance = TRUE)  
  
# 锟节诧拷锟皆硷拷锟较斤拷锟斤拷预锟斤拷  
predictions <- predict(rfModel, newdata = testData)  
  
# 锟介看锟斤拷锟斤拷锟斤拷锟斤拷锟阶既凤拷锟�  
confusionMatrix <- table(predictions, testData$Class)  
accuracy <- sum(diag(confusionMatrix)) / sum(confusionMatrix)  
print(paste("Accuracy:", accuracy))  
  
# 锟介看锟斤拷锟斤拷锟斤拷要锟斤拷  
importance(rfModel)  
  
# 锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷要锟斤拷图  
plot(rfModel, main="Feature Importance")

2.2 锟斤拷锟斤拷预锟斤拷

2.2.1 锟斤拷锟捷硷拷锟斤拷锟斤拷锟斤拷锟斤拷锟捷硷拷锟斤拷锟斤拷锟斤拷为 housingData 锟斤拷

锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷一锟斤拷锟斤拷锟斤拷锟斤拷锟捷硷拷锟斤拷锟斤拷锟叫帮拷锟斤拷锟斤拷锟捷的革拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷囟蔚龋锟斤拷头锟斤拷莸募鄹锟斤拷锟斤拷堑锟侥匡拷锟斤拷锟皆わ拷锟斤拷路锟斤拷莸募鄹锟�

2.2.2 锟斤拷锟斤拷示锟斤拷 

# 锟斤拷锟截憋拷要锟侥帮拷  
library(randomForest)  
  
# 锟斤拷锟斤拷housingData锟窖撅拷锟斤拷锟截碉拷R锟斤拷锟斤拷锟斤拷  
# 锟斤拷锟斤拷锟揭拷锟斤拷锟斤拷源锟斤拷獠匡拷锟斤拷锟皆达拷锟斤拷兀锟斤拷锟絩ead.csv  
  
# 锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷目锟斤拷锟斤拷锟�  
features <- housingData[, -ncol(housingData)] # 锟斤拷锟斤拷锟斤拷锟揭伙拷锟斤拷羌鄹锟�  
prices <- housingData[, ncol(housingData)]  
  
# 锟斤拷锟斤拷训锟斤拷锟斤拷锟酵诧拷锟皆硷拷  
set.seed(123)  
trainIndex <- sample(1:nrow(housingData), nrow(housingData)*0.7)  
trainFeatures <- features[trainIndex, ]  
trainPrices <- prices[trainIndex]  
testFeatures <- features[-trainIndex, ]  
testPrices <- prices[-trainIndex]  
  
# 使锟斤拷锟斤拷锟缴拷锟侥ｏ拷徒锟斤拷锟窖碉拷锟�  
rfModel <- randomForest(trainPrices ~ ., data = data.frame(trainPrices, trainFeatures), ntree = 500, importance = TRUE)  
  
# 锟节诧拷锟皆硷拷锟较斤拷锟斤拷预锟斤拷  
predictedPrices <- predict(rfModel, newdata = data.frame(testPrices = rep(NA, nrow(testFeatures)), testFeatures))  
  
# 锟斤拷锟斤拷预锟斤拷锟斤拷锟斤拷锟斤拷锟界，使锟矫撅拷锟斤拷锟斤拷睿�  
mse <- mean((predictedPrices - testPrices)^2)  
print(paste("Mean Squared Error:", mse))  
  
# 锟介看锟斤拷锟斤拷锟斤拷要锟斤拷  
importance(rfModel)  
  
# 锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷要锟斤拷图  
plot(rfModel, main="Feature Importance")

锟斤拷注锟解，锟斤拷锟斤拷锟斤拷锟斤拷示锟斤拷锟叫碉拷锟斤拷锟捷硷拷锟斤拷 breastCancer 锟斤拷 housingData 锟斤拷锟角硷拷锟斤拷模锟斤拷锟斤拷铱锟斤拷锟斤拷锟揭拷锟斤拷獠匡拷锟斤拷锟皆达拷锟斤拷亍锟斤拷锟斤拷猓拷锟斤拷诜锟斤拷锟皆わ拷猓拷锟斤拷羌锟斤拷锟桔革拷锟斤拷锟斤拷锟斤拷锟捷硷拷锟斤拷锟斤拷锟揭伙拷校锟斤拷锟斤拷锟斤拷锟绞碉拷锟接︼拷锟斤拷锌锟斤拷锟斤拷锟揭拷锟揭伙拷锟斤拷锟斤拷锟斤拷锟皆わ拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷獭锟酵拷锟斤拷锟斤拷锟斤拷森锟街的诧拷锟斤拷锟斤拷锟斤拷 ntree 锟斤拷也锟斤拷锟皆革拷锟捷撅拷锟斤拷锟斤拷锟斤拷锟斤拷械锟斤拷锟斤拷锟�

锟斤拷R锟斤拷锟斤拷锟叫ｏ拷锟斤拷锟角匡拷锟斤拷使锟矫讹拷锟街帮拷锟斤拷锟斤拷锟斤拷预锟解，锟斤拷锟斤拷 randomForest 锟斤拷 caret 锟斤拷 e1071 锟斤拷锟斤拷锟斤拷SVM锟斤拷锟斤拷 glmnet 锟斤拷锟斤拷锟节碉拷锟斤拷锟斤拷锟斤拷毓椋╋拷取锟斤拷锟斤拷锟斤拷医锟斤拷锟斤拷锟斤拷锟斤拷锟绞癸拷锟絉锟斤拷锟皆斤拷锟斤拷预锟斤拷拇锟斤拷锟绞撅拷锟斤拷锟�

2.3 使锟斤拷锟斤拷锟缴拷纸锟斤拷锟皆わ拷锟�

锟斤拷锟饺ｏ拷锟斤拷锟斤拷锟斤拷要锟斤拷装锟斤拷锟斤拷锟斤拷 randomForest 锟斤拷锟斤拷锟斤拷锟斤拷锟轿达拷锟阶帮拷锟斤拷锟� 

# 锟斤拷装randomForest锟斤拷锟斤拷锟斤拷锟斤拷锟轿达拷锟阶帮拷锟�  
install.packages("randomForest")  
  
# 锟斤拷锟斤拷randomForest锟斤拷  
library(randomForest)  
  
# 锟斤拷锟截或创斤拷锟斤拷锟斤拷  
# 锟斤拷锟斤拷锟斤拷锟斤拷使锟斤拷iris锟斤拷锟捷硷拷锟斤拷为示锟斤拷  
data(iris)  
  
# 锟斤拷锟斤拷锟捷硷拷锟斤拷锟斤拷为训锟斤拷锟斤拷锟酵诧拷锟皆硷拷  
set.seed(123) # 为锟剿斤拷锟斤拷目锟斤拷馗锟斤拷锟�  
train_index <- sample(1:nrow(iris), 0.8 * nrow(iris))  
train_data <- iris[train_index, ]  
test_data <- iris[-train_index, ]  
  
# 使锟斤拷训锟斤拷锟斤拷训锟斤拷锟斤拷锟缴拷锟侥ｏ拷锟�  
rf_model <- randomForest(Species ~ ., data = train_data, ntree = 500)  
  
# 使锟矫诧拷锟皆硷拷锟斤拷锟斤拷预锟斤拷  
rf_predictions <- predict(rf_model, newdata = test_data)  
  
# 锟介看预锟斤拷锟斤拷  
print(table(test_data$Species, rf_predictions))  
  
# 锟斤拷锟斤拷预锟斤拷准确锟斤拷  
accuracy <- sum(test_data$Species == rf_predictions) / nrow(test_data)  
print(paste("Accuracy:", accuracy))

2.4 使锟斤拷锟竭硷拷锟截癸拷锟斤拷锟皆わ拷猓拷锟斤拷锟斤拷锟斤拷锟斤拷猓� 

# 锟斤拷锟斤拷MASS锟斤拷锟斤拷锟斤拷锟斤拷锟轿达拷锟阶帮拷锟�  
# MASS锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟竭硷拷锟截癸拷亩锟斤拷锟斤拷锟捷硷拷  
install.packages("MASS")  
library(MASS)  
  
# 使锟斤拷MASS锟斤拷锟叫碉拷Pima Indians Diabetes锟斤拷锟捷硷拷  
data(PimaIndiansDiabetes)  
  
# 锟斤拷锟斤拷锟捷硷拷锟斤拷锟斤拷为训锟斤拷锟斤拷锟酵诧拷锟皆硷拷  
set.seed(123)  
train_index <- sample(1:nrow(PimaIndiansDiabetes), 0.8 * nrow(PimaIndiansDiabetes))  
train_data <- PimaIndiansDiabetes[train_index, ]  
test_data <- PimaIndiansDiabetes[-train_index, ]  
  
# 使锟斤拷训锟斤拷锟斤拷训锟斤拷锟竭硷拷锟截癸拷模锟斤拷  
glm_model <- glm(diabetes ~ ., data = train_data, family = binomial)  
  
# 使锟矫诧拷锟皆硷拷锟斤拷锟斤拷预锟解（注锟解：锟竭硷拷锟截癸拷预锟斤拷锟斤拷歉锟斤拷剩锟斤拷锟揭拷锟轿拷锟斤拷  
glm_probabilities <- predict(glm_model, newdata = test_data, type = "response")  
glm_predictions <- ifelse(glm_probabilities > 0.5, "pos", "neg")  
  
# 锟介看预锟斤拷锟斤拷  
print(table(test_data$diabetes, glm_predictions))  
  
# 锟斤拷锟斤拷预锟斤拷准确锟绞ｏ拷锟斤拷锟斤拷'pos'锟斤拷锟斤拷锟斤拷锟洁，'neg'锟斤拷锟斤拷锟斤拷锟洁）  
accuracy <- sum(test_data$diabetes == (glm_predictions == "pos")) / nrow(test_data)  
print(paste("Accuracy:", accuracy))

2.5 使锟斤拷支锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷SVM锟斤拷锟斤拷锟斤拷预锟斤拷 

# 锟斤拷装e1071锟斤拷锟斤拷锟斤拷锟斤拷锟轿达拷锟阶帮拷锟�  
install.packages("e1071")  
library(e1071)  
  
# 使锟斤拷iris锟斤拷锟捷硷拷  
data(iris)  
  
# 锟斤拷锟斤拷锟捷硷拷锟斤拷锟斤拷为训锟斤拷锟斤拷锟酵诧拷锟皆硷拷  
set.seed(123)  
train_index <- sample(1:nrow(iris), 0.8 * nrow(iris))  
train_data <- iris[train_index, ]  
test_data <- iris[-train_index, ]  
  
# 锟斤拷Species转锟斤拷为锟斤拷锟斤拷锟斤拷锟酵ｏ拷锟斤拷锟斤拷锟轿达拷牵锟�  
train_data$Species <- as.factor(train_data$Species)  
test_data$Species <- as.factor(test_data$Species)  
  
# 使锟斤拷训锟斤拷锟斤拷训锟斤拷SVM模锟斤拷  
svm_model <- svm(Species ~ ., data = train_data, kernel = "radial", cost = 10, gamma = 0.1)  
  
# 使锟矫诧拷锟皆硷拷锟斤拷锟斤拷预锟斤拷  
svm_predictions <- predict(svm_model, newdata = test_data)  
  
# 锟介看预锟斤拷锟斤拷  
print(table(test_data$Species, svm_predictions))  
  
# 锟斤拷锟斤拷预锟斤拷准确锟斤拷  
accuracy <- sum(test_data$Species == svm_predictions) / nrow(test_data)  
print(paste("Accuracy:", accuracy))

锟斤拷锟较达拷锟斤拷示锟斤拷展示锟斤拷锟斤拷锟斤拷锟絉锟斤拷锟斤拷锟斤拷使锟斤拷锟斤拷锟缴拷帧锟斤拷呒锟斤拷毓锟斤拷支锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷预锟解，锟斤拷锟斤拷锟斤拷锟斤拷预锟斤拷准确锟绞★拷锟斤拷注锟解，锟斤拷些示锟斤拷使锟斤拷锟斤拷锟斤拷锟矫碉拷锟斤拷锟捷硷拷

3. 锟斤拷锟缴拷值锟接︼拷锟绞碉拷锟�

3.1 锟轿诧拷锟斤拷锟斤拷菁锟斤拷锟斤拷啵↖ris Dataset Classification锟斤拷

锟轿诧拷锟斤拷锟斤拷菁锟斤拷锟揭伙拷锟斤拷锟斤拷玫姆锟斤拷锟斤拷锟斤拷菁锟斤拷锟斤拷锟斤拷锟�150锟斤拷锟斤拷锟斤拷锟斤拷每锟斤拷锟斤拷锟斤拷锟斤拷锟侥革拷锟斤拷锟斤拷锟斤拷锟斤拷锟洁长锟饺★拷锟斤拷锟斤拷锟斤拷取锟斤拷锟斤拷瓿わ拷取锟斤拷锟斤拷锟斤拷锟饺ｏ拷锟斤拷锟斤拷锟节凤拷锟斤拷锟斤拷锟斤拷锟轿诧拷锟斤拷锟� 

from sklearn.datasets import load_iris  
from sklearn.model_selection import train_test_split  
from sklearn.ensemble import RandomForestClassifier  
from sklearn.metrics import accuracy_score  
  
# 锟斤拷锟斤拷锟轿诧拷锟斤拷锟斤拷菁锟�  
iris = load_iris()  
X = iris.data  
y = iris.target  
  
# 锟斤拷锟斤拷训锟斤拷锟斤拷锟酵诧拷锟皆硷拷  
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  
  
# 锟斤拷锟斤拷锟斤拷锟缴拷址锟斤拷锟斤拷锟�  
clf = RandomForestClassifier(n_estimators=100, random_state=42)  
  
# 训锟斤拷模锟斤拷  
clf.fit(X_train, y_train)  
  
# 预锟斤拷锟斤拷约锟�  
y_pred = clf.predict(X_test)  
  
# 锟斤拷锟斤拷准确锟斤拷  
accuracy = accuracy_score(y_test, y_pred)  
print(f"Accuracy: {accuracy}")

3.2 锟斤拷锟斤拷预锟解（Housing Price Prediction锟斤拷

锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷一锟斤拷锟斤拷锟斤拷锟斤拷锟捷硷拷锟斤拷锟斤拷锟斤拷锟斤拷锟捷碉拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟铰ワ拷锟斤拷锟斤拷龋锟斤拷投锟接︼拷姆锟斤拷邸锟� 

import pandas as pd  
from sklearn.model_selection import train_test_split  
from sklearn.ensemble import RandomForestRegressor  
from sklearn.metrics import mean_squared_error  
  
# 锟斤拷锟斤拷锟斤拷锟捷ｏ拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟揭伙拷锟紺SV锟侥硷拷锟斤拷  
data = pd.read_csv('housing_data.csv')  
X = data.drop('price', axis=1)  # 锟斤拷锟斤拷  
y = data['price']  # 目锟斤拷锟斤拷锟�  
  
# 锟斤拷锟斤拷训锟斤拷锟斤拷锟酵诧拷锟皆硷拷  
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  
  
# 锟斤拷锟斤拷锟斤拷锟缴拷只毓锟斤拷锟�  
rf_regressor = RandomForestRegressor(n_estimators=100, random_state=42)  
  
# 训锟斤拷模锟斤拷  
rf_regressor.fit(X_train, y_train)  
  
# 预锟斤拷锟斤拷约锟�  
y_pred = rf_regressor.predict(X_test)  
  
# 锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷  
mse = mean_squared_error(y_test, y_pred)  
print(f"Mean Squared Error: {mse}")

3.3 锟斤拷影锟斤拷锟斤拷锟斤拷蟹锟斤拷锟斤拷锟絊entiment Analysis of Movie Reviews锟斤拷

锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷一锟斤拷锟斤拷影锟斤拷锟斤拷锟斤拷锟捷硷拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟侥憋拷锟酵讹拷应锟斤拷锟斤拷斜锟角╋拷锟斤拷锟斤拷锟斤拷锟芥）锟斤拷 

from sklearn.datasets import fetch_20newsgroups  
from sklearn.feature_extraction.text import CountVectorizer  
from sklearn.model_selection import train_test_split  
from sklearn.ensemble import RandomForestClassifier  
from sklearn.metrics import classification_report  
  
# 锟斤拷锟斤拷锟斤拷锟捷硷拷锟斤拷锟斤拷锟斤拷使锟斤拷20 Newsgroups锟斤拷锟捷硷拷锟斤拷一锟斤拷锟接硷拷锟斤拷为示锟斤拷锟斤拷  
categories = ['alt.atheism', 'soc.religion.christian']  
newsgroups_train = fetch_20newsgroups(subset='train', categories=categories, shuffle=True, random_state=42)  
X_train, y_train = newsgroups_train.data, newsgroups_train.target  
  
# 锟侥憋拷锟斤拷锟斤拷锟斤拷取锟斤拷锟斤拷锟斤拷使锟矫达拷频锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷  
vectorizer = CountVectorizer()  
X_train_counts = vectorizer.fit_transform(X_train)  
  
# 锟斤拷锟斤拷训锟斤拷锟斤拷锟酵诧拷锟皆硷拷锟斤拷锟斤拷锟斤拷为锟剿简化ｏ拷直锟接达拷训锟斤拷锟斤拷锟叫伙拷锟街ｏ拷  
X_train_counts, X_test_counts, y_train, y_test = train_test_split(X_train_counts, y_train, test_size=0.2, random_state=42)  
  
# 锟斤拷锟斤拷锟斤拷锟缴拷址锟斤拷锟斤拷锟�  
clf = RandomForestClassifier(n_estimators=100, random_state=42)  
  
# 训锟斤拷模锟斤拷  
clf.fit(X_train_counts, y_train)  
  
# 预锟斤拷锟斤拷约锟�  
y_pred = clf.predict(X_test_counts)  
  
# 锟斤拷锟斤拷模锟斤拷  
print(classification_report(y_test, y_pred

3.4 图锟斤拷锟斤拷啵↖mage Classification锟斤拷

锟斤拷然锟斤拷锟缴拷锟酵拷锟斤拷锟街憋拷锟斤拷锟斤拷锟皆硷拷锟斤拷丶锟斤拷锟斤拷图锟斤拷锟斤拷啵拷锟轿拷锟斤拷址锟斤拷锟斤拷诖锟斤拷锟斤拷锟轿拷锟斤拷锟绞憋拷锟斤拷懿锟斤拷锟斤拷锟叫э拷锟斤拷锟斤拷锟斤拷锟斤拷强锟斤拷锟绞癸拷锟斤拷锟斤拷森锟斤拷锟斤拷锟斤拷锟斤拷图锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷HOG锟斤拷SIFT锟斤拷SURF锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟竭达拷预训锟斤拷锟斤拷锟斤拷锟窖澳ｏ拷锟斤拷锟斤拷锟饺★拷锟斤拷锟斤拷锟斤拷锟�

锟斤拷锟斤拷锟斤拷一锟斤拷锟津化碉拷锟斤拷锟接ｏ拷锟斤拷锟斤拷锟斤拷锟斤拷锟窖撅拷锟斤拷锟斤拷一锟斤拷锟斤拷锟斤拷图锟斤拷锟斤拷锟斤拷锟酵讹拷应锟斤拷签锟斤拷锟斤拷锟捷硷拷锟斤拷 

from sklearn.model_selection import train_test_split  
from sklearn.ensemble import RandomForestClassifier  
from sklearn.metrics import classification_report  
import numpy as np  
  
# 锟斤拷锟斤拷锟斤拷锟斤拷锟窖撅拷锟斤拷锟斤拷一锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷X锟斤拷锟斤拷锟界，锟斤拷图锟斤拷锟斤拷锟斤拷取锟斤拷锟斤拷锟斤拷锟斤拷锟酵憋拷签y  
# X = ... (锟斤拷状为 (n_samples, n_features) 锟斤拷NumPy锟斤拷锟斤拷)  
# y = ... (锟斤拷状为 (n_samples,) 锟斤拷NumPy锟斤拷锟斤拷)  
  
# 为锟斤拷锟斤拷示锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟揭恍┠ｏ拷锟斤拷锟斤拷锟�  
n_samples = 1000  
n_features = 64  # 锟斤拷锟斤拷每锟斤拷图锟今被憋拷示为一锟斤拷64维锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷  
X = np.random.rand(n_samples, n_features)  
y = np.random.randint(0, 2, n_samples)  # 锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷  
  
# 锟斤拷锟斤拷训锟斤拷锟斤拷锟酵诧拷锟皆硷拷  
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  
  
# 锟斤拷锟斤拷锟斤拷锟缴拷址锟斤拷锟斤拷锟�  
clf = RandomForestClassifier(n_estimators=100, random_state=42)  
  
# 训锟斤拷模锟斤拷  
clf.fit(X_train, y_train)  
  
# 预锟斤拷锟斤拷约锟�  
y_pred = clf.predict(X_test)  
  
# 锟斤拷锟斤拷模锟斤拷  
print(classification_report(y_test, y_pred))

3.5 锟斤拷锟斤拷锟斤拷要锟斤拷锟斤拷锟斤拷锟斤拷Feature Importance Evaluation锟斤拷

锟斤拷锟缴拷植锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷诜锟斤拷锟酵回癸拷锟斤拷锟今，伙拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷要锟皆★拷锟斤拷锟斤拷锟斤拷锟斤拷锟窖★拷锟酵斤拷锟斤拷模锟酵斤拷锟斤拷浅锟斤拷锟斤拷谩锟� 

# 使锟斤拷之前锟斤拷锟轿诧拷锟斤拷锟斤拷菁锟绞撅拷锟�  
# ...锟斤拷锟斤拷锟斤拷锟斤拷锟捷★拷锟斤拷锟斤拷训锟斤拷锟斤拷锟酵诧拷锟皆硷拷锟斤拷训锟斤拷模锟酵的达拷锟诫）  
  
# 锟斤拷取锟斤拷锟斤拷锟斤拷要锟斤拷  
importances = clf.feature_importances_  
std = np.std([tree.feature_importances_ for tree in clf.estimators_], axis=0)  
indices = np.argsort(importances)[::-1]  
  
# 锟斤拷印锟斤拷锟斤拷锟斤拷锟斤拷  
print("Feature ranking:")  
  
for f in range(X.shape[1]):  
    print("%d. feature %d (%f)" % (f + 1, indices[f], importances[indices[f]]))  
  
# 锟斤拷锟角匡拷锟斤拷使锟斤拷锟斤拷些锟斤拷锟斤拷锟斤拷要锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷图锟斤拷锟斤拷锟竭革拷锟斤拷锟斤拷要锟斤拷选锟斤拷锟斤拷懦锟侥承╋拷锟斤拷锟�

锟斤拷锟较达拷锟斤拷示锟斤拷展示锟斤拷锟斤拷锟缴拷锟斤拷诓锟酵拷锟斤拷锟斤拷碌锟接︼拷茫锟斤拷锟斤拷锟斤拷锟斤拷唷拷毓椤拷锟斤拷锟斤拷锟揭拷锟斤拷锟斤拷锟斤拷取锟阶拷猓拷锟叫┦撅拷锟斤拷械锟斤拷锟斤拷莺锟斤拷锟斤拷锟斤拷锟斤拷锟侥ｏ拷锟侥伙拷蚧模锟绞碉拷锟接︼拷锟斤拷锟斤拷锟斤拷锟斤拷锟揭拷锟斤拷锟斤拷约锟斤拷锟斤拷锟斤拷菁锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷搿�

3.6 锟届常锟斤拷猓∣utlier Detection锟斤拷

锟斤拷锟缴拷锟揭诧拷锟斤拷锟斤拷锟斤拷锟斤拷斐ｏ拷锟斤拷锟斤拷锟饺猴拷锟斤拷狻Ｍ拷锟斤拷锟斤拷锟斤拷锟斤拷森锟斤拷模锟酵诧拷锟斤拷锟斤拷每锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷叶锟节碉拷锟狡斤拷锟斤拷锟斤拷耄拷锟斤拷纾癸拷霉锟斤拷锟缴拷锟� Isolation Forest锟斤拷锟斤拷锟斤拷锟角匡拷锟斤拷识锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟酵拷锟斤拷斐ｏ拷恪�

锟斤拷锟斤拷锟斤拷一锟斤拷使锟斤拷 sklearn-extensions 锟斤拷锟叫碉拷 IsolationForest 锟斤拷锟斤拷锟届常锟斤拷锟斤拷示锟斤拷锟斤拷注锟解： sklearn-extensions 锟斤拷锟斤拷锟斤拷 scikit-learn 锟劫凤拷锟斤拷锟揭伙拷锟斤拷郑锟斤拷锟斤拷峁╋拷锟斤拷锟斤拷频锟绞碉拷郑锟斤拷锟� 

from sklearn_extensions.ensemble import IsolationForest  
import numpy as np  
  
# 锟斤拷锟斤拷 X 锟斤拷锟斤拷锟角碉拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷一些模锟斤拷锟斤拷锟斤拷  
X = np.random.normal(size=(100, 2))  
# 锟斤拷锟斤拷一锟斤拷锟届常锟斤拷  
X = np.r_[X + 2, np.array([[10, 10]])]  
  
# 锟斤拷锟斤拷 IsolationForest 实锟斤拷  
clf = IsolationForest(contamination=0.1)  # 锟斤拷锟斤拷锟斤拷锟捷硷拷锟斤拷锟斤拷10%锟斤拷锟届常锟斤拷  
  
# 锟斤拷锟侥ｏ拷锟�  
clf.fit(X)  
  
# 预锟斤拷锟届常锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷越锟酵ｏ拷越锟斤拷锟斤拷锟斤拷锟届常锟姐）  
y_pred = clf.predict(X)  
scores = clf.decision_function(X)  
  
# 锟斤拷印锟届常锟斤拷锟斤拷锟斤拷预锟斤拷锟斤拷  
for i, s in enumerate(scores):  
    print(f"Sample {i}: Score = {s}, Prediction = {y_pred[i]}")  
  
# 锟斤拷锟角匡拷锟斤拷锟斤拷锟斤拷一锟斤拷锟斤拷值锟斤拷识锟斤拷锟届常锟斤拷  
threshold = -0.5  # 锟斤拷锟斤拷锟街碉拷锟揭拷锟斤拷锟斤拷锟斤拷堑锟斤拷锟斤拷莺锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟�  
outliers = X[scores < threshold]  
print(f"Outliers: \n{outliers}")

锟斤拷注锟解，锟斤拷锟斤拷锟� IsolationForest 锟斤拷锟斤拷懿锟斤拷锟� scikit-learn 锟劫凤拷锟斤拷锟揭伙拷锟斤拷郑锟斤拷锟斤拷锟斤拷强锟斤拷锟绞癸拷锟� scikit-learn 锟叫碉拷 OneClassSVM 锟斤拷 LocalOutlierFactor 锟斤拷实锟斤拷锟斤拷锟狡的癸拷锟杰★拷

3.7 锟斤拷锟角╋拷锟斤拷啵∕ulti-label Classification锟斤拷

锟斤拷锟缴拷锟揭诧拷锟斤拷锟斤拷锟斤拷诙锟斤拷签锟斤拷锟斤拷锟斤拷锟今，硷拷每锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟节讹拷锟斤拷锟斤拷锟斤拷通锟斤拷通锟斤拷使锟矫讹拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟絤ulti-output classifier锟斤拷锟斤拷实锟街ｏ拷锟矫凤拷锟斤拷锟斤拷为每锟斤拷锟斤拷签训锟斤拷一锟斤拷锟斤拷锟斤拷锟侥凤拷锟斤拷锟斤拷锟斤拷 

from sklearn.datasets import make_multilabel_classification  
from sklearn.ensemble import RandomForestClassifier  
from sklearn.metrics import accuracy_score, precision_recall_fscore_support  
  
# 锟斤拷锟斤拷一锟斤拷锟斤拷锟角╋拷锟斤拷锟斤拷锟斤拷菁锟�  
X, y = make_multilabel_classification(n_samples=1000, n_features=20, n_classes=5, n_labels=2, random_state=42)  
  
# 锟斤拷锟斤拷训锟斤拷锟斤拷锟酵诧拷锟皆硷拷  
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)  
  
# 锟斤拷锟斤拷锟斤拷锟缴拷址锟斤拷锟斤拷锟斤拷锟轿匡拷锟斤拷锟角┭碉拷锟揭伙拷锟斤拷锟斤拷锟斤拷锟�  
clf = RandomForestClassifier(n_estimators=100, random_state=42)  
  
# 训锟斤拷模锟斤拷  
clf.fit(X_train, y_train)  
  
# 预锟斤拷锟斤拷约锟�  
y_pred = clf.predict(X_test)  
  
# 锟斤拷锟斤拷每锟斤拷锟斤拷签锟侥撅拷锟饺★拷锟劫伙拷锟绞猴拷F1锟斤拷锟斤拷  
precision, recall, fscore, support = precision_recall_fscore_support(y_test, y_pred, average=None)  
  
# 锟斤拷印锟斤拷锟�  
for i in range(y.shape[1]):  
    print(f"Label {i}: Precision = {precision[i]}, Recall = {recall[i]}, F1 Score = {fscore[i]}")  
  
# 注锟解：锟斤拷锟节讹拷锟角╋拷锟斤拷啵拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷准确锟绞ｏ拷锟斤拷为锟斤拷签之锟斤拷锟斤拷锟斤拷嵌锟斤拷锟斤拷锟�

锟斤拷些示锟斤拷展示锟斤拷锟斤拷锟缴拷锟斤拷诙锟斤拷植锟酵拷锟斤拷锟斤拷碌锟接︼拷茫锟斤拷锟斤拷锟斤拷斐ｏ拷锟解、锟斤拷锟角╋拷锟斤拷锟饺★拷锟斤拷实锟斤拷应锟斤拷锟叫ｏ拷锟斤拷锟角匡拷锟斤拷锟斤拷要锟斤拷锟捷撅拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷菁锟斤拷锟斤拷锟侥ｏ拷偷牟锟斤拷锟斤拷锟斤拷锟斤拷谩锟�

小锟斤拷锟狡硷拷锟侥讹拷

锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟侥斤拷为锟斤拷锟斤拷锟斤拷息锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷同锟斤拷锟睫观碉拷锟街な碉拷锟斤拷锟斤拷锟斤拷锟�

锟斤拷锟斤拷锟狡碉拷锟斤拷锟�

锟斤拷锟斤拷

同锟斤拷锟斤拷锟斤拷

锟斤拷锟斤拷

锟斤拷锟斤拷锟斤拷讯

锟斤拷锟斤拷

  • 锟斤拷戏锟狡硷拷
  • 锟斤拷匕姹�

锟斤拷锟斤拷
  • 锟斤拷讯
  • 锟斤拷频

锟斤拷锟斤拷
  • 锟斤拷卓锟饺帮拷
  • 苹锟斤拷锟饺帮拷

锟斤拷锟节猴拷锟斤拷锟斤拷锟斤拷系锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟�锟斤拷锟斤拷站锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟狡硷拷锟斤拷锟斤拷

频锟斤拷 锟斤拷锟斤拷频锟斤拷锟斤拷锟斤拷锟斤拷专锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷卓锟斤拷锟斤拷锟�锟斤拷app锟斤拷锟斤拷

锟狡硷拷 锟斤拷原锟斤拷锟斤拷锟斤拷锟斤拷锟铰帮拷锟斤拷锟斤拷锟斤拷锟杰匡拷锟斤拷锟斤拷锟截诧拷锟斤拷

锟斤拷锟斤拷 锟斤拷锟斤拷色锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷 锟斤拷锟斤拷锟�锟斤拷锟斤拷战锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷

扫锟斤拷维锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟街伙拷锟芥本锟斤拷

扫锟斤拷维锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷微锟脚癸拷锟节号ｏ拷

锟斤拷站锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟斤拷锟较达拷锟斤拷锟斤拷锟斤拷锟街凤拷锟斤拷陌锟饺拷锟斤拷敕拷始锟�[email protected]

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