This is a model without cross-validation 首先测试一个knn模型,不做CV,不做标准化,不做数据类型转换得到的结果,这里,不转换数据类型会把因子类型的变量舍弃,仅保留数值变量 library(caret)knn.model1 = knn3(V16 ~ .,data = train, k = 5) knn.response1 = predict(knn.model1,test,class = "response...
Note that splitting of instances into training and validation sets follows group boundaries and therefore only approximates the specified train.fraction ratio (the same applies to cross-validation folds). Internally, queries are randomly shuffled before training, to avoid bias. Weights can be used in...
gbm效果和randomForest相近,但是占用内存更少,且支持多核crossValidation运算。 本文用到的处理二值数据的方法,有以下两种: glm(generalized boosted models) glmnet(generalized linear models) glm使用了boosted trees,glmnet使用了regression 代码语言:javascript 复制 # load libraries library(caret) library(pROC) ##...
print(best.iter) # Check performance using 5-fold cross-validation best.iter < - gbm.perf(gbm1, method = "cv") print(best.iter) # Plot relative influence of each variable par(mfrow = c(1, 2)) summary(gbm1, n.trees = 1) # using first tree summary(gbm1, n.trees = best.iter)...
## The best cross-validation iteration was 23.## The best test-set iteration was 75.## There were 7 predictors of which 7 had non-zero influence. 结果表明,交叉验证法的最佳的树的数量是23,测试集法的最佳的树的数量是75. 结果探索 gbm1$fit是训练集的结果,但是并不是直接的类别或者预测概率,...
This is a model without cross-validation 首先测试一个knn模型,不做CV,不做标准化,不做数据类型转换得到的结果,这里,不转换数据类型会把因子类型的变量舍弃,仅保留数值变量 library(caret)knn.model1=knn3(V16~.,data=train,k=5)knn.response1=predict(knn.model1,test,class="response")knn.predict1=ifels...