run_models | R Documentation |
Fit some representative binary classification models.
run_models( .data, target, positive, models = c("logistic", "rpart", "ctree", "randomForest", "ranger", "xgboost", "lasso") )
.data |
A train_df. Train data to fit the model. It also supports tbl_df, tbl, and data.frame objects. |
target |
character. Name of target variable. |
positive |
character. Level of positive class of binary classification. |
models |
character. Algorithm types of model to fit. See details. default value is c("logistic", "rpart", "ctree", "randomForest", "ranger", "lasso"). |
Supported models are functions supported by the representative model package used in R environment. The following binary classifications are supported:
"logistic" : logistic regression by glm() in stats package.
"rpart" : recursive partitioning tree model by rpart() in rpart package.
"ctree" : conditional inference tree model by ctree() in party package.
"randomForest" : random forest model by randomForest() in randomForest package.
"ranger" : random forest model by ranger() in ranger package.
"xgboost" : XGBoosting model by xgboost() in xgboost package.
"lasso" : lasso model by glmnet() in glmnet package.
run_models() executes the process in parallel when fitting the model. However, it is not supported in MS-Windows operating system and RStudio environment.
model_df. results of fitted model. model_df is composed of tbl_df and contains the following variables.:
step : character. The current stage in the model fit process. The result of calling run_models() is returned as "1.Fitted".
model_id : character. Type of fit model.
target : character. Name of target variable.
is_factor : logical. Indicates whether the target variable is a factor.
positive : character. Level of positive class of binary classification.
negative : character. Level of negative class of binary classification.
fitted_model : list. Fitted model object.
library(dplyr) # Divide the train data set and the test data set. sb <- rpart::kyphosis %>% split_by(Kyphosis) # Extract the train data set from original data set. train <- sb %>% extract_set(set = "train") # Extract the test data set from original data set. test <- sb %>% extract_set(set = "test") # Sampling for unbalanced data set using SMOTE(synthetic minority over-sampling technique). train <- sb %>% sampling_target(seed = 1234L, method = "ubSMOTE") # Cleaning the set. train <- train %>% cleanse # Run the model fitting. result <- run_models(.data = train, target = "Kyphosis", positive = "present") result # Run the several kinds model fitting by dplyr train %>% run_models(target = "Kyphosis", positive = "present") # Run the logistic model fitting by dplyr train %>% run_models(target = "Kyphosis", positive = "present", models = "logistic")
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