of variables tried at each split: 7 #每次树的分枝随机抽取7个变量 # # OOB estimate of error rate: 2.63% # 袋外数据估计的错误率是2.63% # Confusion matrix: # Con AD class.error # Con 20 0 0.00000000 #预测错误率是0 # AD 1 17 0.05555556 #预测错误率是0.056 # 画误差和树数量的关系图 ...
STDOUT message(s) from external script: 'data.frame': 1 obs. of 3 variables: $ c..hello..: Factor w/ 1 level "hello": 1 $ X... : Factor w/ 1 level " ": 1 $ c..world..: Factor w/ 1 level "world": 1 可以看到,对 R 语法进行轻微的更改会给结果的架构造成很大的影响。 ...
There are two types of categorical variables: anominal categorical variableand anordinal categorical variable. A nominal variable is a categorical variable without an implied order. This means that it is impossible to say that 'one is worth more than the other'. For example, think of the catego...
'data.frame':2 obs. of 4 variables: $ c1: int 1 -11 $ c2: Factor w/ 2 levels "Hello","world": 1 2 $ c3: Factor w/ 2 levels "6732EA46-2D5D-430B-8A01-86E7F3351C3E",..: 2 1 $ cR: num 4 2 由此,可以看到下面的数据类型转换作为此查询的一部分隐式地执行: ...
()); ^ equality constraints: List<String> upper bounds: ArrayList<String>,Object where R,A,T are type-variables: R extends Object declared in method <R,A>collect(Collector<? super T,A,R>) A extends Object declared in method <R,A>collect(Collector<? super T,A,R>) T extends Object...
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'data.frame':2 obs. of 4 variables: $ c1: int 1 -11 $ c2: Factor w/ 2 levels "Hello","world": 1 2 $ c3: Factor w/ 2 levels "6732EA46-2D5D-430B-8A01-86E7F3351C3E",..: 2 1 $ cR: num 4 2 From this, you can see that the following data type conversions were impli...
of 4 variables: $ patientID: num 1 2 3 4 $ age : num 25 34 28 52 $ diabetes : Factor w/ 2 levels "Type1","Type2": 1 2 1 1 $ status : Ord.factor w/ 3 levels "Excellent"<"Improved"<..: 3 2 1 3 NULL > print(summary(patientdata))*显示对象的统计概要 patientID age ...
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