tidylearn (version 0.1.0)

tl_model: Create a tidylearn model

Description

Unified interface for creating machine learning models by wrapping established R packages. This function dispatches to the appropriate underlying package based on the method specified.

Usage

tl_model(data, formula = NULL, method = "linear", ...)

Value

A tidylearn model object containing the fitted model ($fit), specification, and training data

Arguments

data

A data frame containing the training data

formula

A formula specifying the model. For unsupervised methods, use ~ vars or NULL.

method

The modeling method. Supervised: "linear" (stats::lm), "logistic" (stats::glm), "tree" (rpart), "forest" (randomForest), "boost" (gbm), "ridge"/"lasso"/"elastic_net" (glmnet), "svm" (e1071), "nn" (nnet), "deep" (keras), "xgboost" (xgboost). Unsupervised: "pca" (stats::prcomp), "mds" (stats/MASS/smacof), "kmeans" (stats::kmeans), "pam"/"clara" (cluster), "hclust" (stats::hclust), "dbscan" (dbscan).

...

Additional arguments passed to the underlying model function

Details

The wrapped packages include: stats (lm, glm, prcomp, kmeans, hclust), glmnet, randomForest, xgboost, gbm, e1071, nnet, rpart, cluster, and dbscan. The underlying algorithms are unchanged - this function provides a consistent interface and returns tidy output.

Access the raw model object from the underlying package via model$fit.

Examples

# \donttest{
# Classification -> wraps randomForest::randomForest()
model <- tl_model(iris, Species ~ ., method = "forest")
model$fit  # Access the raw randomForest object

# Regression -> wraps stats::lm()
model <- tl_model(mtcars, mpg ~ wt + hp, method = "linear")
model$fit  # Access the raw lm object

# PCA -> wraps stats::prcomp()
model <- tl_model(iris, ~ ., method = "pca")
model$fit  # Access the raw prcomp object

# Clustering -> wraps stats::kmeans()
model <- tl_model(iris, method = "kmeans", k = 3)
model$fit  # Access the raw kmeans object
# }