library(tidymodels) # for the rsample package, along with the rest of tidymodels
library(modeldata) # for the cells data
data(cells, package = "modeldata")
cells %>%
count(class) %>%
mutate(prop = n/sum(n))
预测结果变量分布情况，为二分类变量
#以下按照结果变量进行分层随机分组
set.seed(123)
cell_split <- initial_split(cells %>% select(-case),
strata = class,
prop =0.75)
cell_train <- training(cell_split)
cell_test <- testing(cell_split)
#以下查看建模组和测试组结果变量比例情况
cell_train %>%
count(class) %>%
mutate(prop = n/sum(n))
# test set proportions by class
cell_test %>%
count(class) %>%
mutate(prop = n/sum(n))
两组class的比例一样
以下用分类决策树模型构建模型
#定义模型及损失函数
rf_mod <-
rand_forest(trees = 1000) %>%
set_engine("ranger") %>%
set_mode("classification")
#在训练集进行训练
set.seed(234)
rf_fit <-
rf_mod %>%
fit(class ~ ., data = cell_train)
rf_fit
模型参数
#在训练集上进行模型的好坏的参数验证
rf_training_pred <-
predict(rf_fit, cell_train) %>% #预测分类结果
bind_cols(predict(rf_fit, cell_train, type = "prob")) %>% #预测分类概率
# 以下添加原始真实结果
bind_cols(cell_train %>%
select(class))
rf_training_pred
训练集上预测结果情况
#查看训练集AUC及准确率
rf_training_pred %>% # training set predictions
roc_auc(truth = class, .pred_PS)
rf_training_pred %>% # training set predictions
accuracy(truth = class, .pred_class)
训练集AUC及准确率情况
#以下用上述同样的方法计算测试集的AUC和准确率
rf_testing_pred <-
predict(rf_fit, cell_test) %>%
bind_cols(predict(rf_fit, cell_test, type = "prob")) %>%
bind_cols(cell_test %>% select(class))
rf_testing_pred %>% # test set predictions
roc_auc(truth = class, .pred_PS)
rf_testing_pred %>% # test set predictions
accuracy(truth = class, .pred_class)
测试集情况
#以下将进行交叉验证
#采用10成交叉验证
set.seed(345)
folds <- vfold_cv(cell_train, v = 10)
#定义分析流程
rf_wf <-
workflow() %>%
add_model(rf_mod) %>%
add_formula(class ~ .)
#利用10成交叉验证拟合训练集
set.seed(456)
rf_fit_rs <-
rf_wf %>%
fit_resamples(folds)
#显示拟合结果
collect_metrics(rf_fit_rs)
交叉验证拟合结果
