Linear Regression
Usage
linearRegression(
algo = "Linear Regression",
engine = "glmnet",
mode = "regression",
trainingData = NULL,
splitedData = NULL,
formula = NULL,
rec = NULL,
v = 5,
gridNum = 5,
iter = 10,
metric = "rmse",
seed = 1234
)Arguments
- algo
A name of the algorithm which can be customized by user (default: "Linear Regression").
- engine
The name of software that should be used to fit the model ("glmnet" (default), "lm", "glm", "stan").
- mode
The model type. It should be "classification" or "regression" ("regression" (default)).
- trainingData
The training data.
- splitedData
A data frame including metadata of split.
- formula
formula for modeling
- rec
Recipe object containing preprocessing information for cross-validation.
- v
Applying v-fold cross validation in modeling process (default: 5).
- gridNum
Initial number of iterations to run before starting the optimization algorithm.
- iter
The maximum number of search iterations.
- metric
Metric to evaluate the performance (classification: "roc_auc" (default), "accuracy" / regression: "rmse" (default), "rsq").
- seed
Seed for reproducible results.