run_models {alookr} | 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")
```

[Package *alookr* version 0.3.9 Index]