emil (version 1.1-6)

modeling.procedure: Setup a modeling procedure

Description

A modeling procedure is an object containing all information necessary to carry out and evaluate the performance of a predictive modeling task with fit, tune, or evaluate.modeling. To use an out-of-the box algorithm with default values, only the method argument needs to be set. See emil for a list of available methods. To deviate from the defaults, e.g. by tuning variables or using a custom function for model fitting, set the appropriate parameters as described below. For a guide on how to implement a custom method see the documentaion page emil.extensions.

Usage

modeling.procedure(method, param = list(), error.fun = NULL, fit.fun,
  predict.fun, vimp.fun)

Arguments

method

The name of the modeling method. Only needed to identify plug-in functions, i.e. if you supply them yourself there is no need to set method.

param

A list of model parameters. These will be fed to the fitting function after the dataset (x and y parameters). To tune a parameter, supply the candidate values in a vector or list.

When tuning more than one parameter, all c

fit.fun

The function to be used for model fitting.

predict.fun

The function to be used for model prediction.

vimp.fun

The function to be used for calculating or extracting variable importance scores.

error.fun

Performance measure used to evaluate modeling procedures and to tune parameters. See error.fun for details.

Value

• An object of class modeling.procedure.

See Also

emil, evaluate.modeling, fit, tune, predict, vimp

Examples

# 1: Fit linear discriminants without tuning any parameter,
# since it has none
modeling.procedure("lda")

# 2: Tune random forest's `mtry` parameter, with 3 possible values
modeling.procedure("randomForest", list(mtry = list(100, 250, 1000)))

# 3: Tune random forest's `mtry` and `maxnodes` parameters simultaneously,
# with 3 values each, testing all 9 possible combinations
modeling.procedure("randomForest", list(mtry = list(100, 250, 1000),
                                         maxnodes = list(5, 10, 25)))

# 4: Tune random forest's `mtry` and `maxnodes` parameters simultaneously,
# but only test 3 manually specified combinations of the two
modeling.procedure("randomForest", list(list(mtry = 100, maxnodes = 5),
                                    list(mtry = 250, maxnodes = 10),
                                    list(mtry = 1000, maxnodes = 25)))

# 5: Tune elastic net's `alpha` and `lambda` parameters. Since elastic net's
# fitting function can tune `lambda` internally in a more efficient way
# than the general framework is able to do, only tune `alpha` and pass all
# `lambda` values as a single argument.
modeling.procedure("glmnet", list(alpha = seq(0, 1, length.out=6),
                                   lambda = list(seq(0, 5, length.out=30))))

# 6: Train elastic nets using the caret package's model fitting framework
library(caret)
modeling.procedure("caret", list(method = "glmnet",
    trControl = list(trainControl(verboseIter = TRUE, classProbs = TRUE))))