iris 是r語言內置的數據集

head(iris) # 與python的不同iris.head() Sepal.LengthSepal.WidthPetal.LengthPetal.WidthSpecies

5.1	3.5	1.4	0.2	setosa
4.9	3.0	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5.0	3.6	1.4	0.2	setosa
5.4	3.9	1.7	0.4	setosa

# 查看數據的行和列 dim(iris)

150

5

# 數據的類型 mode(iris)

‘list’

# columns的名字 names(iris)

'Sepal.Length'

'Sepal.Width'

'Petal.Length'

'Petal.Width'

'Species'

# r是data.frame py是pandas.Dateframe str(iris) 'data.frame': 150 obs. of 5 variables:$ Sepal.Length: num 5.1 4.9 4.7 4.6 5 5.4 4.6 5 4.4 4.9 ...$ Sepal.Width : num 3.5 3 3.2 3.1 3.6 3.9 3.4 3.4 2.9 3.1 ...$ Petal.Length: num 1.4 1.4 1.3 1.5 1.4 1.7 1.4 1.5 1.4 1.5 ...$ Petal.Width : num 0.2 0.2 0.2 0.2 0.2 0.4 0.3 0.2 0.2 0.1 ...$ Species : Factor w/ 3 levels "setosa","versicolor",..: 1 1 1 1 1 1 1 1 1 1 ... # 查看數據集的屬性 attributes(iris)

# 數據的概述 summary(iris) Sepal.Length Sepal.Width Petal.Length Petal.Width Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300 Median :5.800 Median :3.000 Median :4.350 Median :1.300 Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800 Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500 Species setosa :50 versicolor:50 virginica :50 # 查看分類的種類 table(iris$Species) setosa versicolor virginica 50 50 50 # 畫圖 Sepal萼片長度 hist(iris$Sepal.Length)

# 密度分布圖 plot(density(iris$Sepal.Length))

# 花萼長度散點圖 plot(iris$Sepal.Length,iris$Sepal.Width)

plot(iris)

# 邏輯回歸 只能分兩類 a<-which(iris$Species=='virginica') head(a) # 對應的編號

101

102

103

104

105

106

# 取出其他的兩類 myir <- iris[-a,] # 數據分樣 測試和訓練 s <- sample(100,80) # 100抽80 # 排序 s <- sort(s) ir_trian <- myir[s,] head(ir_trian) Sepal.LengthSepal.WidthPetal.LengthPetal.WidthSpecies134579

5.1	3.5	1.4	0.2	setosa
4.7	3.2	1.3	0.2	setosa
4.6	3.1	1.5	0.2	setosa
5.0	3.6	1.4	0.2	setosa
4.6	3.4	1.4	0.3	setosa
4.4	2.9	1.4	0.2	setosa

ir_test <- myir[-s,] model <-glm(Species~.,family = binomial(link="logit"),data= ir_trian) summary(model) Call: glm(formula = Species ~ ., family = binomial(link = "logit"), data = ir_trian)Deviance Residuals: Min 1Q Median 3Q Max -1.570e-05 -2.110e-08 2.110e-08 2.110e-08 1.865e-05 Coefficients:Estimate Std. Error z value Pr(>|z|) (Intercept) 4.691 681526.322 0 1 Sepal.Length -9.568 216769.252 0 1 Sepal.Width -7.254 99870.123 0 1 Petal.Length 18.946 153746.614 0 1 Petal.Width 25.341 222619.596 0 1(Dispersion parameter for binomial family taken to be 1)Null deviance: 1.1070e+02 on 79 degrees of freedom Residual deviance: 1.0579e-09 on 75 degrees of freedom AIC: 10Number of Fisher Scoring iterations: 25 # 殘差 a<- predict(model,type="response") # 大于0.5 為1 res_train <- ifelse(a>0.5,1,0) b<- predict(model,type="response",newdata=ir_test) res_test <- ifelse (b>0.5,1,0) model <- glm(Species~.,family = binomial(link = "logit"),data= ir_trian,control= list(maxit=100)) summary(model) Call: glm(formula = Species ~ ., family = binomial(link = "logit"), data = ir_trian, control = list(maxit = 100))Deviance Residuals: Min 1Q Median 3Q Max -9.535e-06 -2.110e-08 2.110e-08 2.110e-08 1.132e-05 Coefficients:Estimate Std. Error z value Pr(>|z|) (Intercept) 5.292e+00 1.125e+06 0 1 Sepal.Length -1.013e+01 3.577e+05 0 1 Sepal.Width -7.501e+00 1.645e+05 0 1 Petal.Length 1.988e+01 2.534e+05 0 1 Petal.Width 2.634e+01 3.667e+05 0 1(Dispersion parameter for binomial family taken to be 1)Null deviance: 1.1070e+02 on 79 degrees of freedom Residual deviance: 3.8911e-10 on 75 degrees of freedom AIC: 10Number of Fisher Scoring iterations: 26

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